bluuebunny/crossref_metadata_2025 huggingface dataset:
ОСОБЛИВОСТІ ПОВОДЖЕННЯ З ВІДХОДАМИ БУДІВНИЦТВА ТА ЗНОСУ В РЕГІОНАХ УКРАЇНИ
Authors: САФРАНОВ Тамерлан , ПРИХОДЬКО Вероніка , КОРБУТ Марія
Published: September 2024

За умов російського вторгнення в Україну постає необхідність адаптації сфери управління відходами до військового стану, зокрема, через зміни нормативно-законодавчої сфери та управлінські рішення щодо відходів війни, серед яких виділяється окрема категорія – відходи від руйнувань. Перед регіонами України стоїть важливе завдання розробити ефективні підходи до перероблення та повторного використання будівництва, зносу та руйнування. Для впровадження принципів циркулярної економіки необхідно системно подолати низку глибинних викликів, що перешкоджають налагодженню ефективної моделі управління та поводження з відходами будівництва, зносу та руйнування. Метою дослідження є аналіз особливостей поводження з відходами будівництва та зносу в регіонах України. Методологія дослідження базується на результатах досліджень, які аналізують доступні способи та перспективи поводження з відходами будівництва та зносу в регіонах України. Наукова новизна роботи полягає в тому, що вперше проаналізовано систему управління відходами руйнації, будівництва та зносу в регіонах України та надано практичні рекомендації щодо її вдосконалення. Залучення відходів від руйнувань в загальнонаціональну стратегію управління відходами є необхідною умовою забезпечення екологічної безпеки та сталого розвитку регіонів України. Висновки. На сьогодні стан сфери управління відходами стикнувся з проблемою відходів від руйнувань, обсяги яких значні і продовжують зростати. Але в умовах дешевого захоронення і відсутності зацікавленості у переробці таких відходів очікуваним є засмічення внаслідок розміщення на земельних ділянках, перевантаження полігонів ТПВ та нагромадження великих обсягів таких відходів на спеціальних ділянках з довгостроковою перспективою перетворення на полігони будівельних і інертних відходів.
СОЦІОЛІНГВІСТИЧНА І СОЦІОКУЛЬТУРНА КОМПЕТЕНТНІСТЬ ПУБЛІЧНИХ СЛУЖБОВЦІВ
Authors: ДРОЗД Тетяна , ЖАРАЯ Світлана , НІКОЛАЄЦЬ Юлія
Published: September 2024

Стаття має на меті надати теоретичне обґрунтування особливостей соціолінгвістичної та соціокультурної компетентності, довести їх важливість у процесі як організації публічними службовцями міжнародної співпраці, так і час фахової підготовки здобувачів вищої освіти спеціальності «Публічне управління та адміністрування». Методологію дослідження склали методи наукового пізнання, аналітичної обробки інформації й системного аналізу. З огляду на Європейську довідкову рамку ключових компетентностей для навчання протягом життя, потрактовано поняття, які розкривають сутнісний зміст лінгвістичної, соціальної, культурної компетентностей. Встановлено, що зазначені концептуальні положення знаходять відображення у вітчизняній нормативно-правовій базі, у науково-методичному доробку з питань розроблення стандартів вищої освіти, у чинних локальних документах щодо професійної діяльності публічних службовців та освітньо-професійних програмах вищої освіти. Потрактовано понятійно-термінологічний апарат дослідження, виокремлено компоненти соціолінгвістичної компетентності й акцентовано увагу на необхідності розуміння соціальної стратифікації мови, що забезпечує регуляцію взаємин між представниками різних соціальних статусів, поколінь, статей, національностей, регіонального походження, професійної належності тощо. Закцентовано увагу на важливості невербальних засобів комунікації, значення яких може суттєво різнитися в окремих національних і соціальних групах. У контексті характеристики «соціокультурної компетентності» введено поняття фонових знань комунікантів, тобто розуміння і транслювання відомостей культурного, матеріально-історичного, географічного характеру, уявлення про побут, традиції, звичаї тощо. Наукова новизна роботи полягає в тому, що вперше представлено компоненти соціолінгвістичної і соціокультурної компетентності як інтегровані в цілісну систему розвитку комунікативної компетентності публічних службовців та здобувачів фахової вищої освіти. На підставі теоретичного аналізу надано рекомендації щодо розвитку зазначених компетентностей під час навчання у закладі вищої освіти. Висновки. Комунікативна, зокрема соціолінгвістична й соціокультурна компетентність публічного управлінця синтезує в собі загальну культуру та її специфічні прояви у професійній управлінській діяльності. Очікуваним результатом набуття компетентності має бути функційність мовця, усвідомлення й відображення у мовленнєвій практиці взаємозв’язку між мовою, культурою, явищами соціальної дійсності, налагодження конструктивної міжкультурної комунікації. Належна увага до розвитку соціолінгвістичної та соціокультурної компетентності публічних управлінців, починаючи з фахової освіти, дозволить підвищити рівень професійності й надасть можливість реалізувати себе в якості професіонала зокрема й у галузі міжнародної співпраці, що є вкрай необхідним для України в період війни й повоєнного відновлення.
FINANCIAL DIGITALIZATION: ROLE AND SIGNIFICANCE FOR BANKING SERVICES
Authors:   State Scientific Research Institute for Informatization and Economic Modeling, Boldov Аndriy
Published: September 2024

This article is devoted to summarizing information about the opportunities that arise as a result of the implementation and improvement of financial digitalization, its role and significance in the field of banking services. It describes a number of transformations of the financial sphere due to the influence of digital technologies on it, which are designed to form an efficient and convenient system of services for its participants. During the work on these materials, it was discovered was revealed that the banking sector, despite the negative external influence, adapted to the realities of today, taking advantage of all the advantages of digitalization, and provided round-the-clock access to all services for customers, enabling the implementation of everyday transactions. These materials also reveal some priority areas of banks' work in the direction of strengthening digitalization, which are currently seen in the digital optimization of back-office processes aimed at reducing costs and saving products, accelerating their implementation and modification, as well as improving interaction with clients; the use of predictive analytics that allows for more accurate credit scoring and more effective risk management, reducing costs and increasing lending profitability; development of electronic payment technologies and investment in digitalization of corporate payment systems. These materials describe innovative models of banks that implement further development, improvement and implementation of digitalization in customer services, affecting the change in business models and the development of the bank's open ecosystem. The article also reveals the principles of AI in the work of banks and describes a number of threats that may arise when working with digital services. Special attention is paid to cyber security, as a key element of data and information protection. In the end, the priority areas of both scientific and practical research were identified in such areas as determining the need for banks to become more competitive in the digital environment, providing clear and high-quality online services, as well as taking into account global trends in the banking sector. In this context, emphasis is placed on the need to increase investment in the latest technologies, the rapid implementation of innovative services, and taking into account the impact of the pandemic and geopolitical events on the banking sector.
Colour Enhanced Time/Pressure Envelope (CETPE), una nuova rappresentazione del suono digitale su schermo
Authors: Brizio Cesare
Published: September 2024

Le più diffuse forme di visualizzazione del suono digitale comprendono gli Inviluppi Tempo/Pressione (TPE, anche detti "oscillogrammi"), le Analisi Frequenza/Pressione (FPA), e le Immagini Spettrografiche Tempo/Frequenza (TFSI, spesso citate come "spettrogrammi"). Solo queste ultime, grazie alla mappatura a colori dei valori di pressione, sono in grado di fornire simultaneamente informazioni visuali sui tre domini numerici (tempo, asse orizzontale; frequenza, asse verticale; pressione: colore) che definiscono digitalmente il fenomeno acustico. Qui viene proposto il Colour Enhanced Time/ Pressure Envelope (CETPE), una forma mappata su colore RGB a 24-bit RGB del comune TPE disponibile a schermo in qualsiasi software per l'audio digitale: questa nuova rappresentazione basata sulle Fast Fourier Transform (FFT), controllata da parametri definiti dall'utente, è capace di fornire informazioni parziali ma potenzialmente rilevanti sulla presenza e sull'intensità di interessanti bande di frequenza in un file audio. Un dimostratore proof-of-concept è stato sviluppato in linguaggio Python, e un esempio di schermo di output, coerente con l'aspetto atteso della rappresentazione, è incluso in questo articolo.
Attempting to Understand Why Canadian Drinking Water Advisories Are Still in Place: A Statistical Analysis of Indigenous Communities Under Long-Term Drinking Water Advisories
Authors: Taghizadeh Christopher
Published: September 2024

Drinking water advisories (DWAs) continue to be an issue for many Canadian Indigenous communities. An audit was done in 2011 that found variables that could cause an advisory; however, a lack of quality drinking water still persists. In 2016, the Trudeau Government attempted to bring 166 long-term DWAs (LT-DWAs) to an end. While many successes were reported, too many DWAs still remain. This research builds upon the 2011 audit and provides new variables not previously identified. A quantitative analysis was conducted using data on LTDWA communities from government databases and Indigenous websites. This list consisted of analyzing 20 communities that have the oldest LT-DWA to date and 20 earliest communities to come off a LT-DWA. From analysis, three variables were key to communities remaining under a LT-DWA: I) large gender ratio gaps, II) the investment amount, and III) the amount of workers traveling outside their census.
Development of an Extensive Soot Prediction Methodology Using Neural Networks
Authors: Adebisi Jack
Published: September 2024

For the last few decades, it has been one of the top priorities for the combustion field to study soot formation carefully. Thus, soot modeling is one of the most important aspects in understanding the process of soot formation, which in turn contributes to the development of effective soot mitigation processes. This research project discusses past methods of soot modeling as well as the results of computational analysis methods performed on flame data. The study in this thesis uses the Soot Estimator neural network model developed by Jadidi et. al [M. Jadidi, L. Di Liddo, S. Dworkin, Energies 13(18), (2020)]. In this study, the work done by the Jadidi study is expanded on by increasing the number of flame data types used and the number of flame properties translated into their Lagrangian histories in two studies. This increase was made to see the feasibility of neural network predictions in processing more robust flame data. These Lagrangian flame property histories were then processed by another network known as the Multi-layer Perceptron (MLP) Regressor. The purpose of this thesis is two-fold; discussions on past methods of soot modelling and an exploration of experimental methods performed. The discussion aspect of this paper explored the history of artificial intelligence in combustion studies. It closely examined their successes and weaknesses and compared various classification methods with application to soot prediction. The main novel aspect of this paper investigated the process of developing a neural network that predicts soot volume fraction with high accuracy via training with specific variables. The results in the first and second study showed that high accuracy results can be obtained. The supercomputing aspect of the second study also showed potential for real world applications, while accommodating economic and computational constraints.
LGBTQ+ Representation in Hindi Cinema After the Decriminalization of Homosexuality
Authors: Surve Krushnalee
Published: September 2024

The LGBTQ+ community has been stigmatized and discriminated against in all parts of the world, including India. The decriminalization of homosexuality by the Supreme Court of India in 2018 was a historic win for the LGBTQ+ community's fight for equality. Yet, everyday acceptance remains elusive, including representations in popular culture. Media content shapes the beliefs and values of people, having not only objective effects on society, but also reinforcing the social function of discrimination. Since India is a major producer of, as well as market for movies, this paper aims to study the representation of the LGBTQ+ community in Hindi cinema, this includes both the commercial and non-commercial Hindi movies produced for national distribution across India. The study aims to discover how the 2018 judgment affected the decisions of the Central Board of Film Certification (CBFC) as well the quality of representation in the films themselves. The main objective of this study is to compare examples of representations of queer characters in mainstream Hindi cinema before and after the 2018 judgment decriminalizing homosexuality in a thematic content analysis to identify negative stereotypes and qualities of positive representations. The findings are presented in the form of an illustrated argument, which allows readers to learn visually and form opinions about the issue by directly comparing the difference in queer representation before and after decriminalization.
Efficient Precondition Evaluation for Lifted Progression Planning
Authors: Young Ryan
Published: September 2024

The popular approach in modern automated AI planning is to transform a given planning problem to a grounded transition system before attempting to solve it. This approach struggles on 'difficult-to-ground' planning problems which include a large number of objects and action schemas with many arguments since the transition system may not fit in memory. An alternative approach to planning from a theorem-proving perspective works directly with lifted action schemas during runtime to find which ground actions are possible in a given state. This alleviates issues with difficult-to-ground problems, but currently, evaluating action preconditions at runtime to find next possible actions is computationally expensive. In this thesis, I introduce two new methods for evaluating action preconditions more efficiently. These methods rely on algorithms for conjunctive query answering, namely the HashJoin and LFTJ algorithms developed in database management systems. They help alleviate a computational bottleneck faced by the theorem-proving approach at runtime. I also improve a domain-independent heuristic based on the 'planning graph' data structure which aids lifted planners in determining promising action sequences. All methods have been experimentally evaluated and compared on a large collection of planning benchmarks. I summarize experimentally determined division of work related to precondition evaluation that can be done offline vs computations that can be done at runtime. My thesis draws conclusions about the next steps in efficient evaluation of action preconditions.
Video Analytic Data Reduction Model for Edge Computing
Authors: Pudasaini Dipak
Published: September 2024

The traditional approaches for video analytics use cloud computing for data processing. Employing deep learning in video analytics and transferring large volumes of data causes network congestion and high latency in centralized cloud computing systems. To minimize these problems, we propose and implement a Video Analytic Data Reduction Model (VADRM) that divides the video streaming jobs into smaller deep learning tasks for processing on edge computing. VADRM provides a distributed model for reducing processing requirements in each phase of the video analytics applications employing deep learning methods. In this dissertation, deep learning using the Convolutional Neural Network (CNN) is implemented for building a prototype of VADRM. We improve the object tracking phase by proposing a CNN-based object reidentification model in the VADRM prototype. The experimental results show that the proposed reidentification method increases the quality of object tracking compared to the existing methods. Simulation of video analytics is challenging because of the lack of real data generated by machine learning techniques in the new generations of these applications. In this dissertation, the iFogSim simulator is used with the real data of the VADRM prototype to evaluate the module deployment algorithms for the edge devices. The experimental results show that the edge network usage and latency are improved by 12% using our proposed module placement algorithm compared to the iFogSim Edge-ward algorithm. The data collected from the VADRM prototype are characterized to find the distribution models for developing the analytical and simulation models. Large artificial data are generated using these distribution models to conduct a resource management simulation comparing edge and cloud computing for deep learning-based video analytics applications.
Inflation, Financial Uncertainty, and Capital Markets: A Comparative Case of US, UK, and China
Authors: Zarfsazi Arash
Published: September 2024

How do inflation and financial uncertainty impact capital markets over time? In this paper, we analyze the stock index from three major capital markets (US S&P 500, UK FTSE100, and China SSE) to find their time-varying co-integration magnitude with inflation and financial uncertainty, while controlling for other macroeconomic variables including oil price, gold price, and foreign exchange rate. We further advance to include sectoral analysis in the US market and study 11 sub-groups of S&P500 to find the largest inflation-affected industries. The CBOE volatility indices' relationship with inflation and financial uncertainty are also examined using ARFIMA models. The VAR-Granger analysis is used to find the causality direction among the market indices and the inflation levels. Our findings illustrate statistically significant and positive co-integration among all three indices and their respective inflation levels. On the sector level, our paper suggests higher co-integration among inflation and value sectors rather than the growth sectors. We have also conducted an interrupted time series analysis to determine the 2010 post-financial crisis Feds regulation on the hedging capability of the financial sector. Our study confirms financial uncertainty to be a statistically significant and positive driver of US market volatility (VIX) and relevant ETFs. Granger causality tests show bidirectional causality between inflation and index level in the US and UK, whereas in China we are experiencing a unidirectional causality from the capital market to the inflation levels.
Navigating the Challenges of Mobile Big Data: The Case of Michigan's Super-Regional Shopping Centres
Authors: Azmy Ali
Published: September 2024

The increase of spatial big data has created unprecedented opportunities for business decisionmakers to leverage data analytics to gain a more intimate level of knowledge of their consumers and leverage that knowledge to advance their market position. Mobile big data is utilized to evaluate how super-regional shopping centres faired during the COVID-19 pandemic and the subsequent recovery period. Metrics such as dwell time, visitor count, and visitation count were derived from the data source and subsequently analyzed to forecast traffic in a post-pandemic scenario. The results indicate that shifts in consumer behaviour occurred, given that shopping centre visitations decreased during the recovery period after the mandated lockdowns. The study highlights both the technical and management limitations of location-enabled mobile data in estimating forecast models.
Automated Focal Seizure Localization by Joint Processing EEG and MRI Data of Epileptic Patients
Authors: Taki Sarina
Published: September 2024

Localizing epilepsy in the brain has attracted a lot of attention in diagnosing and treating epileptic seizures and is currently a major challenge. Existing methods of epilepsy localizers heavily rely on the physician expertise to estimate the location by observing the EEG, and recently the fMRI. No automated method for this diagnosis exists. This paper proposes an innovative method of combining the mutual information of EEG and MRI to develop an automated epilepsy source locator. The proposed method, denoted by ULeV-EsLORETA, includes three components: Multimodal Denoiser (ULeVMSEE), Forward Model, and Efficient High Resolution sLORETA (EHR-sLORETA). The ULeVMSEE algorithm is based on two methods: Unified Left Eigenvectors (ULEV) and Mean Squared Eigenvalues Error (MSEE). ULEV has the ability to extract the mutual information of EEG and MRI and separates the desired mutual information from the additive disturbance of the recorded data. Additionally, MSEE is employed to estimate the number of common bases based on EEG. As pre-processing methods for ULEV, standardization and resizing techniques are taken into consideration, to ensure that the multimodal datasets are compatible with one another and may be processed simultaneously. Following ULeVMSEE, the extracted bases are used to estimate the noise-free EEG using a forward model. At the end, Efficient High Resolution sLORETA (EHR-sLORETA) is utilized to extract the location of seizure sources. ULeV-EsLORETA is compared with the existing source locator methods for both synthetic and real data. The method illustrated solid advantages over the existing methods in the sense of Average Correlation Coefficient (ACC) and Average Error Estimation (AEE) and estimation of the true number. of sources. It is shown that ACC is improved on average by 17.6% and 40% for different number of common sources and various SNR-difference respectively. Additionally, AEE is improved on average by 56% in terms of estimating the source location. While the existing approaches over or under estimate the number of true sources occasionally, the proposed method shows complete accuracy in this estimation. Our automated results on 26 epileptic patients was in full agreement with the manual diagnosis of the epileptologist.
Learning Neural Representations for Dynamic Heterogeneous Graphs
Authors: Nguyen Hoang
Published: September 2024

Many real-world networks, such as social networks and academic networks, contain structural heterogeneity and experience temporal evolution. However, while there has been growing literature on network representation learning, only a few have addressed the need to learn representations for dynamic heterogeneous networks. The objective of our work in this thesis is to introduce DyHNet, which learns representations for such networks and distinguishes itself from the state-of-the-art by systematically capturing different semantics, (1) local node semantics, (2) global network semantics, and (3) longer-range temporal associations between network snapshots when learning network representations. We conduct experiments consisting of the two most common downstream graph learning tasks on four real-world datasets. Our results demonstrate that the proposed method is able to show consistently better and more robust performance compared to the state-of-the-art techniques.
Retweet Prediction Using a DNN Model With Community-Based Attention
Authors: Yang Xiu
Published: September 2024

One of the modes of information dissemination on Twitter micro-blogging platform is via "Retweet". Ideas, comments and information posted by an author can be relayed further across spheres. Prediction of retweet is an important field, especially due to the recent spotlight on the role of social media in dissemination of ideologies and the creation of echo chambers. In this work, we propose the use of attention mechanism on a retweet graph-embedding to focus on community networks of Twitter users. Along with a generated profile vector for tweet author and the current user, as well as a generated word embedding from tweet content, we also generate community embeddings from a retweet graph containing social context among the author, user and user's neighbourhood. The community embeddings form the input to the attention layer, the output of which is reduced by a CNN layer to become an influence vector. All of these form a concatenated input into a fully connected network, capped by a softmax layer to produce a binary classification for the retweet prediction. We then compare our proposed model against several baseline models, evaluate against a balanced dataset and specific feature sets, both implicit and explicit. Our results show that the use of attention mechanism to incorporate social graph based on retweet behaviour is promising and provides a positive yield for recall and f1-scores compared to baseline prediction approaches.
Sequence Covering and Packing Arrays
Authors: Javed Muhammad
Published: September 2024

Combinatorial testing evolved from the statistical design of experiments, and has proven to be an effective and economical method of testing. A combinatorial test suite given by a sequence covering array provides tests so that every occurrence of every t or fewer events is tested with every possible ordering. In this thesis, we investigate sequence covering arrays and related structures known as sequence packing arrays. A sequence covering (packing) array is a set B of N k-sequences on v events, where k ≤ v. In a sequence covering (packing) array every subsequence of t or fewer events, where t ≤ k, appears in at least (most) λ of the sequences in B. The number of tests in a minimum (maximum) size sequence covering (packing) array is called the sequence covering (packing) array number. The goal is usually to minimize (maximize) the size of a sequence covering (packing) array. In the literature, sequence covering (packing) array numbers are only known for small values of k, or for the case when k = v. In this thesis, we determine values of small sequence covering (packing) array numbers for the cases when k < v, λ = 1 and t = 2. We introduce direct and recursive construction strategies. For N ∈ {4, 5, 6, 7, 10, 11}, we determine the set of pairs (v, k) for which the sequence covering number is equal to N. For N ∈ {2, 3, 4, 5}, we also determine the set of pairs for which the sequence packing number is equal to N. Moreover, for each N ∈ {7, 8, 9} we determine sets of pairs (v, k) for which the sequence packing number is at most N. For N ∈ {3, 4, 5, 6, 7, 8, 9}, we were able to improve known bounds on sequence packing array numbers. In most applications, some interactions are infeasible due to system constraints. A set of constraints C is a set of sequences which should not be tested by a test suite given by a sequence covering array. A C-compliant sequence covering array provides tests so that every occurrence of every t or fewer events is tested with every possible ordering, except for the ones given in C. The number of blocks in a minimum size C-compliant sequence covering array is called the C-compliant sequence covering array number. In this thesis, we determine bounds on the number in the case where C is a set of ordered pairs, particularly when the constraint graph induced by C is a subgraph of an oriented multipartite graph. An algorithm to generate constrained sequence covering arrays is also given.
Investigating a Deep Energy Retrofit Package for Nova Scotia's Affordable Housing Sector
Authors: Jacobs Millie
Published: September 2024

The construction industry is a significant contributor of greenhouse gas emissions in developed countries around the world. The building sector has adopted new ideas and initiatives in recent years to help tackle climate change, but the move toward net zero has been gradual. This is particularly troublesome in countries with cold climates, like Canada, where fossil fuels are used extensively for space heating. This study investigated an optimal deep energy retrofit package for an affordable housing building owned by the Housing Trust of Nova Scotia to determine its constructibility, effectiveness, financial feasibility, as well as scalability and repeatability for buildings of similar archetype. This project was intended to act as a proof-of-concept to address much needed deep energy upgrades to Canada's existing building stock, with particular focus on affordable housing. The objective was to create an exterior deep energy retrofit package that ensures high performance, minimal disruption to occupants and fosters a "pride of place" for its residents. The results indicated a 95% reduction in space heating demand and projected thermal comfort improvement by including a cooling system. Upgrades to water use equipment for the entire building decreased the water system energy demand by 60% when compared to the baseline model. Similarly, the energy use intensity for the proposed retrofit shows a significant reduction from both the baseline and the ENERGY STAR benchmark for multifamily residential buildings, with an energy use intensity (EUI) of 62 kWh/m². Furthermore, the annual greenhouse gas emissions reduced from 57 kgCO₂/year to 2.47 kgCO₂/year, illustrating the significance that deep energy retrofits have on contributing to the achievement of federal and provincial GHG emission targets. Additionally, the methodology investigated in this research implies that buildings of the same archetype are easily repeatable and scalable. Some design details of the panels require consideration with buildings that have more complex geometry and building components, such as balconies. Overall, the deep energy retrofit package has a high likelihood of replication and application to similar buildings.
The Effect of Moisture Accumulation in Below-Grade Insulation
Authors: Osmond Jesse
Published: September 2024

As building codes continue to introduce more stringent performance requirements, the placement of insulation in the building enclosure has been shifted to the exterior to provide a continuous insulation layer and reduce thermal bridging. However, modern construction practices for foundation walls typically leave the below-grade insulation exposed to the exterior environment, resulting in the insulation layer absorbing moisture from the soil. This moisture absorption will decrease the long-term performance of insulation and is typically not accounted for in the design, resulting in buildings performing below the expected level. The magnitude of this reduction will vary depending on the type of insulation used. This study utilized hygrothermal modelling and laboratory testing to determine how significant this reduction in performance will be for various types of insulation typically used below-grade. The insulation types investigated in this study include EPS, graphite embedded EPS (GPS), XPS, mineral wool, and closed-cell spray foam. The results showed a minimal increase in thermal conductivity amongst the rigid polystyrene insulation products, while the thermal conductivities of the spray foam and mineral wool insulations increased by approximately 10% and 20% respectively. The minimal effect on the polystyrene insulation products is likely a consequence of the lower permeabilities of these products. Additional research should be conducted over a longer wetting period. In cold climates, the effect of freeze-thaw cycling on these insulations should also be studied.
Point-Of-Interest Recommendations: Learning Potential Check-Ins From Networks
Authors: Bashir Syed Raza
Published: September 2024

The emergence of Location-based Social Network (LBSN) services presents a great opportunity to develop personalized Point-of-Interest (POI) recommender systems. Although a personalized POI recommender system can significantly improve users' experience for outdoor activities, it faces numerous challenges, including the availability of real POIs, and the difficulty of addressing user-location cold-start issues. To address these issues, we propose a two-step framework for leveraging POI information into a recommender system. First, we propose learning a set of real locations where an individual has previously checked in. The goal is to detect real and fake POIs and feed the factual information into the subsequent steps. Then, in order to address the user/location cold-start issues, we incorporate rich contextual information related to users' check-ins into a recommender system. We propose using different deep neural network-based methods to build recommender systems. The first approach is based on a two-encoder approach, while the second approach is based on the Transformer-based model BERT. The goal is to evaluate the efficacy of various methods for achieving our objectives. To assess the proposed model, we run extensive experiments on real-world data sets using a variety of state-of-the-art baseline methods and evaluation metrics. The experimental results show that our methods are effective for detecting real POIs, with an accuracy of 94 percent, a balanced F1-score of 95 percent, and a Hit Ratio score of around 92 percent during top@50 for the recommendation task.
Distributed ML-Based Network Traffic Classification Using a Data Parallelization Approach
Authors: Abid Hussain Muhammad Musa K
Published: September 2024

The ever-increasing internet data leads to very large datasets which benefits ML/DL models for more accurate classification, increased diversity for classification of different types of network traffic, and handling anomaly traffic for prevention of potential cyber-attacks. We have deployed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), advanced hybrid Convolutional LSTM (ConvLSTM), Convolutional GRU (ConvGRU), and XGBoost algorithm using Data Parallelization approach. Data Parallelization allows for faster training times on large datasets as shown in our results, and it is also beneficial in the cloud-edge environment by allowing efficient distribution of computation and data across multiple nodes for improved performance of edge devices. The experimental setup was implemented in the cloud and parallel training was executed using Nvidia Tesla Graphics Processing Units (GPUs). Lastly, comparison of the performance metric results is presented between the non-parallel centralized (single node) and data parallel distributed (two nodes and four nodes) approaches.
A deep learning approach to the measurement of the nucleus-to-cytoplasm ratio
Authors: Anstey Zachary
Published: September 2024

Cancer is commonly diagnosed by experts examining tissue samples using a microscope. Cellular features (e.g., nucleus) can be isolated and examined using dyes. Unusually large or small nuclei are a characteristic of many types of cancerous cells, leading to the development of the nucleus-to-cytoplasm ratio. Manually assessing the nucleus-to-cytoplasm ratio tends to be biased.

The nucleus-to-cytoplasm ratio can be assessed using imaging flow cytometry images and fluorescent dyes. Imaging flow cytometry can be used to quickly image large numbers of cells, making it an ideal candidate for use with deep learning. Deep learning and imaging flow cytometry have been used for tasks such as classifying cells into mitotic phases.

This thesis aims to use deep learning to assess the nucleus-to-cytoplasm ratio using imaging flow cytometry brightfield images. Examining whether the nucleus-to-cytoplasm ratio can be assessed without fluorescent dyes. Results suggest that deep learning can successfully assess the nucleus-to-cytoplasm ratio using brightfield images, with results comparable to imaging flow cytometry analysis, producing AUC values above 0.9.
Classifying Dental Caries Types Using Panoramic Dental Images Using Watershed Method and Multiclass Support Vector Machine
Authors: Putra, S.Pd., M.T. Rangga Pahlevi Putra , Rahman Aviv Yuniar
Published: September 2024

Teeth are one of the calcified and hard structures found in the human mouth. One of the tooth defects that often appears and is experienced by several people in the world is damage caused by dental caries. Diseases that can arise from dental caries include swelling of the gums and fever in the body. To classify and determine the level of damage in dental disease, dentists usually utilise examinations through dental panoramic images. Dental panoramic images are digital images of x-rays that can help provide a lot of information about teeth such as cavities or tooth structure. However, the problem that occurs sometimes to identify or classify the type of caries is still found to be a mismatch of analysis so that technological aids are needed to provide analysis or decision support. Therefore, by applying digital image processing technology by applying methods in image processing, namely Watershed segmentation and the Multiclass Support Vector Machine method, it is possible to classify the type of caries using dental panoramic images. From the results of the research conducted, it can be explained that the results of segmentation of dental panoramic images using the Watershed method can show the detected caries area spots. Meanwhile, the use of the Multiclass SVM method for the classification method shows accuracy results reaching 88%.
Czynniki skutecznej współpracy w modelu potrójnej helisy w ujęciu SEM
Authors: Bojar Matylda , Czarnocki Krzysztof J. , Czarnocka Elżbieta
Published: September 2024

Współcześnie współpracę w warunkach potrójnej helisy między nauką, biznesem i władzą traktuje się jako jeden z kluczowych czynników wpływający na rozwój innowacji w warunkach regionalnych. Pomimo uznania skuteczności tego modelu, istnieją liczne bariery, które utrudniają efektywną współpracę tych podmiotów. Jednym z problemów badań prowadzonych od ponad 10 lat przez zespół Politechniki Lubelskiej jest próba zidentyfikowanie i zrozumienie znaczenia głównych barier współdziałania w ramach modelu potrójnej helisy (Triple Helix). Wykorzystując różnorodne metody badawcze, takie jak analiza literatury, badania terenowe liderów poszerzone o pogłębione wywiady oraz analizę danych, udało się wykazać istnienie kluczowych przeszkód w procesie efektywnego współdziałania zespołów międzysektorowych działających w regionach. Wyniki wskazują, że bariery te kształtują się na poziomie barier prawnych, organizacyjnych, w zakresie kompetencji, ale również kapitału społecznego. Zaprezentowane w tym artykule rozważania są kontynuacją analiz zmierzających do stworzenią koncepcji modelu efektywnego przywództwa w strukturze potrójnej helisy, w turbulentnym otoczeniu rynkowym. Jako środek do weryfikacji spójności i dobroci odwzorowania determinant działania (aktywności i skuteczności) lidera w turbulentnym otoczeniu, w pracy zostało przyjęte modelowanie równań strukturalnych (SEM) (Structural Equation Modeling). Wnioski z tego badania dostarczają istotnych wskazówek dla decydentów politycznych, instytucji akademickich i przedsiębiorstw, jakie czynniki mają decydujący wpływ na skuteczne współdziałanie między sektorami w modelu potrójnej helisy dla rozwoju innowacji, a tym samym wzrostu gospodarczego regionów.
Nodular lymphocyte-predominant Hodgkin lymphoma: advances in disease biology, risk stratification, and treatment
Authors: Salvaris Ross T. , Allanson Benjamin M. , Collins Graham , Cheah Chan
Published: September 2024

Recent updates have detailed how patients with nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) may be better risk stratified using prognostic scoring systems. Most patients with NLPHL present with early-stage disease and have an indolent disease course. To reflect these differences from classic Hodgkin lymphoma, nomenclature has been updated to recognise nodular lymphocyte predominant B-cell lymphoma as an alternative to NLPHL. The Global NLPHL One Working Group have published their pivotal dataset in 2024 which challenges the prognostic significance of variant immunoarchitectural (IAP) patterns and proposes a new prognostic scoring system. Key identified prognostic factors include age >45 years, stage III-IV disease, haemoglobin
Worksop Digital Marketing Promosi Penjualan Keripik Daun Kelor Hasil Industri Kreatif Mahasiswa Universitas Indo Global Mandiri
Authors: Heriyati Agustina , Afriyani Fauziah , Romli Harsi , Pratiwi Putri Indah , Mulyati Mulyati
Published: September 2024

Keripik daun kelor merupakan produk inovatif dari industri kreatif mahasiswa Universitas Indo Global Mandiri  yang memiliki potensi besar untuk berkembang di pasar lokal maupun internasional. Namun, tantangan utama yang dihadapi adalah kurangnya pengetahuan dan keterampilan dalam digital marketing yang dapat mendukung promosi dan penjualan produk secara efektif. Workshop ini diselenggarakan dengan tujuan untuk meningkatkan kapasitas mahasiswa dalam menggunakan strategi digital marketing untuk memasarkan keripik daun kelor. Kegiatan ini mencakup pelatihan intensif mengenai pembuatan konten kreatif, pengelolaan media sosial, optimisasi mesin pencari (SEO), dan pemanfaatan e-commerce. Tujuan pengabdian ini adalah menyediakan sesi mentoring dan praktik langsung untuk membantu peserta dalam mengaplikasikan ilmu yang didapatkan. Hasil pengabdian masyarakat  dapat meningkatkan pengetahuan mahasiswa mengenai digital marketing, memperluas jangkauan pasar, serta meningkatkan penjualan keripik daun kelor sebagai produk unggulan mahasiswa.
On a Class of Difference Equations System of Fifth-Order
Authors: Kara Merve , Yazlik Yasin
Published: September 2024

In the current paper, we investigate the following new class of system of difference equations \begin{align} u_{n+1}=&f^{-1}\left( g\left( v_{n-1}\right) \frac{A_{1}f\left( u_{n-2}\right)+B_{1}g\left( v_{n-4}\right) }{C_{1}f\left( u_{n-2}\right)+D_{1}g\left( v_{n-4}\right)}\right), \nonumber \\ v_{n+1}=&g^{-1}\left( f\left( u_{n-1}\right) \frac{A_{2}g\left( v_{n-2}\right)+B_{2}f\left( u_{n-4}\right) }{C_{2}g\left( v_{n-2}\right)+D_{2}f\left( u_{n-4}\right)}\right) ,\ n\in \mathbb{N}_{0}, \nonumber \end{align} where the initial conditions $u_{-p}$, $v_{-p}$, for $p=\overline{0,4}$ are real numbers, the parameters $A_{r}$, $B_{r}$, $C_{r}$, $D_{r}$, for $r\in\{1,2\}$ are real numbers, $A_{r}^{2}+B_{r}^{2}\neq 0\neq C_{r}^{2}+D_{r}^{2}$, for $r\in\{1,2\}$, $f$ and $g$ are continuous and strictly monotone functions, $f\left( \mathbb{R}\right) =\mathbb{R}$, $g\left( \mathbb{R}\right) =\mathbb{R}$, $f\left( 0\right) =0$, $g\left( 0\right) =0$. In addition, we solve aforementioned general two dimensional system of difference equations of fifth-order in explicit form. Moreover, we obtain the solutions of mentioned system according to whether the parameters being zeros or not. Finally, we present an interesting application.
Artificial Intelligence’s Role in Student Plagiarism: A Graduate University’s Model of Best Practices
Authors: Halbert James D. , DiMatteo-Gibson Donna , Cabrera Marianne , Mazurowski Tricia , Ingram Maleka
Published: September 2024

This white paper discusses a model of best practices to better identify and address plagiarism issues with students using AI. It serves as an example to help younger institutions that may not have a policy in place to recognize the importance of hitting this head-on. By creating a taskforce, we were able to quickly come to a resolution for a university that has three campuses in Chicago, Online, and in Vancouver, BC. We also share best practices that will help current professors and core faculty alike in dealing with plagiarism from students using AI in their work. We end with a discussion of examples that support this effort.
7Cups’ Support Services: A Critical Examination of the Risks and Ethical Concerns
Authors: Gilmore Keagan
Published: September 2024

This critical examination scrutinizes the risks and ethical concerns surrounding 7Cups, an online platform providing emotional support to individuals globally. Through firsthand experience and scientific analysis, this report highlights several issues within 7Cups’ support services, including the facilitation of gender transition in teens, shortcomings in group chat room moderation, and inadequacies in listener training. Concerns arise regarding the potential harm caused by hasty recommendations for gender transition without proper evaluation, the lack of supervision in group chat rooms leading to ineffective support, and the absence of accountability and oversight for listeners, potentially resulting in harmful advice. The report underscores the need for a more personalized and comprehensive approach to emotional support, prioritizing ethical principles, and ensuring the safety and effectiveness of mental health services provided by platforms like 7Cups.
Hybrid Leadership and Incivility in Healthcare
Authors: Sparks Betsy , McCann Jack
Published: September 2024

With a rise in incivility in the workplace, leadership is critical for today’s healthcare organizations. There are many leadership styles to consider for healthcare leaders. This research focuses on nine leadership and hybrid leadership styles to determine significant relationships to incivility among healthcare employees. We used the Vannsimpco Leadership Survey and Chung et al.’s Work Group Incivility scale to survey 174 healthcare employees. Our study found that managers need to know that one leadership style may not be the best and that a combination of styles may be needed in varying situations. We found that transformational and autocratic-transformational leadership styles reduced incivility between healthcare employees. Our study indicated that when a manager uses transformational leadership or a hybrid form of autocratic-transformational leadership, healthcare employees have less incivility.
The Influence of Servant Leadership on Employee Turnover Intention: Employees Want to Be Heard
Authors: Yarbrough Jason , Hyatt Katherine , Smith Stephen
Published: September 2024

Employee turnover is expensive. This qualitative single-case study examined the effectiveness of the servant leadership characteristic of listening concerning employee turnover intention in the manufacturing industry. The study sampled seventy randomly selected employees from a cross section of a manufacturing plant from each generation using a structured five-point Likert scale questionnaire. Findings suggest the servant leadership characteristic of listening is critical to reducing employee turnover intention in millennials in this industry. Employees wanted to be listened to, cared about, and for leaders to consider their opinions. Servant leaders practice listening, inclusion in decision-making, and development of employees.
Crime of Sexual Violence Against Children (Study at Kebumen Police Station)
Authors: Setiyawan Deni , Noor Rahmad
Published: September 2024

In Indonesia, cases of sexual violence have increased every year, the victims are not only adults but now have spread to teenagers, children and even toddlers. The purpose of this research focuses on identifying and analysing the obstacles faced by investigators and the efforts made by investigators in revealing cases of sexual violence against children. This research uses empirical methods through interview data and field observations, then conceptually analysed to find obstacles and efforts of investigators in overcoming cases of sexual violence in children. The results of the study found that the main obstacle for investigators in uncovering criminal acts of sexual violence against children is the lack of information from children who experience sexual violence due to trauma. The absence of a psychologist in the investigating team, difficulties in bringing in a psychologist when a case of child abuse occurs. Efforts made by Kebumen Police investigators are to call a psychologist when there is a case of sexual violence against a child. Efforts to provide assistance by psychologists, and traumatic recovery for children who experience sexual violence. It is hoped that the Kebumen police will have a field of psychology experts who specialise in handling child sexual abuse crimes in Kebumen.