bluuebunny/crossref_metadata_2025 huggingface dataset:
A Particle Fusion Approach for Distributed Filtering and Smoothing
Authors: Lin Tony X. , Coogan Samuel , Sofge Donald A. , Zhang Fumin
Published: December 2023

Distributed state estimation is an important tool for coordinated team decision-making and typically involves sharing information between robots in order to outperform individual state estimation. The shared information typically takes the form of relative measurements which allow team members to act as virtual sensors for other robots where the virtual sensor uncertainty is corrupted also by the team member’s state uncertainty. However, incorporating relative measurements commonly depends on a pointwise product operation in most distributed estimation techniques which is well defined for continuously-valued distributions but ill-defined for particle-based distributions. We propose a drop-in replacement for the pointwise product based on using the generalized Hölder’s inequality to upper-bound the product over a series of grid cell sets that discretize the state space. This upper-bound is well defined for particle-based distributions and allows for tighter approximations by decreasing the volume of the sets. We leverage the approach to realize two distributed estimation strategies that use a pointwise product, the Kullback–Leibler Average and Belief Propagation and use these methods in simulations and experiments with a pair of miniature autonomous blimps. We found that after distributed smoothing, we were able to achieve an average improvement of ∼25.1% and ∼35.2% in the position tracking error, indicating our distributed smoothing strategy is able to improve the tracking performance of our initial filtering estimates.
Parameter Identification of a Large-Scale Vibroacoustic Finite Element Model with a One-Dimensional Convolutional Neural Network
Authors: Cram Sophie , Yu Jiale , Luegmair Marinus , Maeder Marcus , Marburg Steffen
Published: December 2023

Uncertainties are significant in the early vibroacoustic development, e.g., of a car body, to prevent costly modifications close to the start of production. First, engineers must know which uncertain parameters are sensitive: Our previous work identified 170 uncertain parameters being sensitive out of a complex finite element model with 1,300 uncertain parameters – a parameter reduction of approximately 87%. Second, engineers aim to find reliable distributions of these sensitive input parameters for finite element simulations. Finding these distributions is very demanding in a large-scale vibroacoustic model with several connecting parameters, as research already acknowledges regarding simplified connections. In this paper, we address this challenge with neural networks. For this, we use data in the frequency domain. Due to the curse of dimensionality, it is difficult to determine the parameter set of 170 parameters with a neural network. Therefore, we examine the influence of the number of parameters on the performance of neural networks. Furthermore, we train a fully connected feed-forward neural network and compare this to a one-dimensional convolutional neural network. The latter exhibits a better performance. Finally, we show how to determine distributions of the analyzed parameters based on artificial measurement data. Due to this process, we can significantly improve our finite element simulations and show how to deal with the challenge of determining uncertain parameters in a large-scale vibroacoustic finite element model based on data in the frequency domain.
Computational Studies of Multi-Target Directed Ligands Against Acetylcholinesterase, Butyrylcholinesterase and Amyloid Beta as Potential Anti-Alzheimer’s Agents
Authors: Maurya Neha , Gaddam Mareechika , Sharma Abha
Published: December 2023

The designed flavone-hydroxypyridinone derivatives were screened in silico with the multiple targets of Alzheimer’s disease (AD) for their binding affinity. In this study, 29 novel derivatives of flavone-hydroxypyridinone were docked with acetylcholinesterase, butyrylcholinesterase and amyloid beta to determine their binding interactions on these targets of AD. The designed ligands were passed through the Lipinski and Veber filter, of which 29 ligands (8 from series 1 and 21 from series 2) obeyed the rule. All the molecules were docked with all the targets. To refine and confirm the docking study results, the stability of complexes is verified using molecular dynamics simulations. As our aim is to develop Multi-Target Direct Ligand (MTDL), one ligand from both the series has been selected as MTDL, is one of the best-docked ligands, which interacts with the protein through hydrogen bond, Vander Waal interactions, [Formula: see text]-[Formula: see text] stacking and [Formula: see text]-alkyl interactions. Where amino acid residues such as Leu17, Lys16, Phe20 and Ala21 play important role in receptor–ligand interactions. The studies imply that the best-docked MTDL complex has a good binding affinity which has been further confirmed by molecular dynamic simulations and different pharmacokinetics parameters calculated. The study provides guidance for the development of flavone-hydroxypyridinone derivatives as MTDL against AD.
High Throughput FPGA-Based Object Detection via Algorithm-Hardware Co-Design
Authors: Anupreetham Anupreetham , Ibrahim Mohamed , Hall Mathew , Boutros Andrew , Kuzhively Ajay , Mohanty Abinash , Nurvitadhi Eriko , Betz Vaughn , Cao Yu , Seo Jae-Sun
Published: December 2023

Object detection and classification is a key task in many computer vision applications such as smart surveillance and autonomous vehicles. Recent advances in deep learning have significantly improved the quality of results achieved by these systems, making them more accurate and reliable in complex environments. Modern object detection systems make use of lightweight convolutional neural networks (CNNs) for feature extraction, coupled with single-shot multi-box detectors (SSDs) that generate bounding boxes around the identified objects along with their classification confidence scores. Subsequently, a non-maximum suppression (NMS) module removes any redundant detection boxes from the final output. Typical NMS algorithms must wait for all box predictions to be generated by the SSD-based feature extractor before processing them. This sequential dependency between box predictions and NMS results in a significant latency overhead and degrades the overall system throughput, even if a high-performance CNN accelerator is used for the SSD feature extraction component. In this paper, we present a novel pipelined NMS algorithm that eliminates this sequential dependency and associated NMS latency overhead. We then use our novel NMS algorithm to implement an end-to-end fully pipelined FPGA system for low-latency SSD-MobileNet-V1 object detection. Our system, implemented on an Intel Stratix 10 FPGA, runs at 400 MHz and achieves a throughput of 2,167 frames per second with an end-to-end batch-1 latency of 2.13 ms. Our system achieves 5.3× higher throughput and 5× lower latency compared to the best prior FPGA-based solution with comparable accuracy.
Report on the 8th International Workshop on Mining Actionable Insights from Social Networks (MAISoN'22) - Special Edition on Mental Health and Social Media at TheWebConf 2022
Authors: Bagheri Ebrahim , Inkpen Diana , Yang Christopher C. , Zarrinkalam Fattane
Published: December 2023

The eighth edition of the workshop on Mining Actionable Insights from Social Networks (MAISoN 2022) took place virtually on April 26th, 2022, co-located with the ACM Web Conference 2022 (TheWebConf 2022). This year, we organized a special edition with focus on mental health and social media. The aim of this edition was to bring together researchers from different disciplines to discuss research that goes beyond descriptive analysis of social media data and instead investigate different techniques that use social media data for building diagnostic, predictive and prescriptive analysis models for mental health applications. This topic attracted a lot of interest from the community especially because of all the considerations surrounding the impact of social media during the COVID-19 pandemic which has impacted on people's mental health issues.

Date:
26 April 2022.

Website:
https://2022.maisonworkshop.org/.
Adversarial Machine Learning in Recommender Systems
Authors: Merra Felice Antonio
Published: December 2023

Recommender systems are ubiquitous. Our digital lives are influenced by their use when, for instance, we select the news to read, the product to buy, the friend to connect with, and the movie to watch. While enormous academic research efforts have been mainly focused on getting high-quality recommendations to reach maximum user satisfaction, little effort has been devoted to studying the integrity and security of these systems. Is there an underlying relationship between the characteristics of the historical user-item interactions and the efficacy of injection of false users/feedback strategies against collaborative models? Can public semantic data be used to perform attacks more potent in raising the recommendability of victim items? Can a malicious user poison or evade the image data of visual recommenders with adversarial perturbed product images? Is the family of model-based recommenders more vulnerable to multi-step gradient-based adversarial perturbations? Furthermore, is the adversarial training robustification still effective in the last scenario? Is this training defense influencing the beyond-accuracy and bias performance?

This dissertation intends to pave the way towards more robust recommender systems, beginning with understanding how to robustify a model, what is the cost of robustness in terms of reduction of recommendation accuracy, and which are the novel adversarial risks of modern recommenders. This thesis, getting inspiration from the literature on the security of collaborative models against the insertion of hand-engineered fake profiles and the recent advances of adversarial machine learning methods in other research areas like computer vision, contributes to several directions: (i) the proposal of a practical framework to interpret the impact of data characteristics on the robustness of collaborative recommenders [Deldjoo et al., 2020], (ii) the design of powerful attack strategies using publicly available semantic data [Anelli et al., 2020], (iii) the identification of severe adversarial vulnerabilities of visual-based recommender models where adversaries can break the recommendation integrity by pushing products to the highest recommendation positions with a simple and human-imperceptible perturbation of products' images [Anelli et al., 2021b], (iv) the proposal of robust adversarial perturbation methods capable of completely breaking the accuracy of matrix factorization recommenders [Anelli et al., 2021a], and (v) a formal study that examines the effects of adversarial training in reducing the recommendation quality of state-of-the-art model-based recommenders Anelli et al. [2021c].

Awarded by:
Politecnico di Bari, Bari, Italy on 24 January 2022.

Supervised by:
Tommaso DI Noia.

Available at:
https://iris.poliba.it/retrieve/dd89f8a6-faa4-ccdd-e053-6605fe0a1b87/Adversarial_Machine_Learning_in_Recommender_Systems.pdf.
Recent Advances in Algorithmic Problems for Semigroups
Authors: Dong Ruiwen
Published: December 2023

In this article we survey recent progress in the algorithmic theory of matrix semigroups. The main objective in this area of study is to construct algorithms that decide various properties of finitely generated subsemi-groups of an infinite group
G
, often represented as a matrix group. Such problems might not be decidable in general. In fact, they gave rise to some of the earliest undecidability results in algorithmic theory. However, the situation changes when the group
G
satisfies additional constraints. In this survey, we give an overview of the decidability and the complexity of several algorithmic problems in the cases where
G
is a low-dimensional matrix group, or a group with additional structures such as commutativity, nilpotency and solvability.
Complete and Efficient Higher-Order Reasoning via Lambda-Superposition
Authors: Bentkamp Alexander , Blanchette Jasmin , Nummelin Visa , Tourret Sophie , Waldmann Uwe
Published: December 2023

Superposition is a highly successful proof calculus for reasoning about first-order logic with equality. We present λ-superposition, which extends superposition to higher-order logic. Its design goals include soundness, completeness, efficiency, and gracefulness with respect to standard first-order superposition. The calculus is implemented in two automatic theorem provers: E and Zipper position. These provers regularly win trophies at the CADE ATP System Competition, confirming the calculus's applicability. This paper is a summary of research that took place between 2017 and 2022.
SIGLOG Monthly 242
Authors: Purser David
Published: December 2023

October 2023 edition of SIGLOG Monthly, featuring deadlines, calls and community announcements.
Understanding the Improved Sodium Ion Storage in Wood-Derived Hard Carbon Anodes by Hydrogen Treatment
Authors: Lou Xiaohang , Cao Yi , Guo Shuai , Han Huawei , Jiang Xiubao , Siqing Sudu , long Zhen , Zhu Xianchao , Qiu Xiaoqing
Published: December 2023

Biomass carbon, as a renewable resource, has the ability to be a hard carbon anode material for sodium-ion batteries. Its performance is highly reliant on the surface functional group. Through our work, successfully synthesized the high-performance hard carbon by the treatment of the hydrogen reduction process of rose willow. Moreover, the effects of hydrogen reduction on the evolution of functional groups and the relevant electrochemical performance have been investigated. After undergoing hydrogen reduction treatment, hard carbons’ surface features and layer spacing were greatly enhanced. In addition, the partial surface C=O group was reduced to C-O, which led to the Na+ adsorption active sites and pseudo-capacity increased, thus improving the dynamics of the electrode process. As anticipated, the resulting hard carbon exhibited a capacity of 325 mAh g−1, with an initial coulomb efficiency (ICE) of 80.84%. This study is in an effort to demonstrate the possibility of biomass-based carbon materials in preparation for future commercial applications of sodium-ion batteries.
Performance Analysis of Electrochemical Detection Platform for DNA Hybridization Using TGN-Based Nanobiosensor
Authors: Rahmani Meisam
Published: December 2023

Trilayer graphene nanoribbon (TGN) has great potential in related biomedical applications such as drug and gene delivery and deoxyribonucleic acid (DNA) sensing. The performance of a biosensor based on a liquid gate TGN field effect transistor (Lg-TGNFET) to different DNA cancentrations and adsorption effect on the sensing parameters are analytically investigated in this research. Also, the energy band structure, density of states, carrier concentration, electrical conductance and caused electrical response on TGN as a detection element are studied considering the spin–orbit coupling effect, in which significant current change of the TGN-based biosensor is observed after exposure to DNA different values. DNA concentration as a function of gate voltage is assumed and sensing factor is defined. The results of this research confirm that the current of the biosensor is decreased and Vg-min is obviously left-shifted by increasing value of DNA concentration, suggesting that DNA molecules n-dopes the TGN film. In order to verify the accuracy of the TGN-based biosensor, it is compared with recent analytical and experimental reports on DNA biosensors. The proposed biosensor with high specificity and sensitivity exposes higher current compared to that of monolayer graphene counterpart for analogous ambient conditions.
Optimizing Gas Sensing Properties of Fe-Loaded Indium Zinc Oxide Nanocomposite for Efficient Detection of Ammonia Gas
Authors: Krishna Kurugundla Gopi , Parne Saidi Reddy , Nagaraju P.
Published: December 2023

The current study delineates the role of gas sensing properties of Fe-IZO (Iron loaded Indium Zinc oxide) a mixed metal oxide nanocomposite. The heterostructured Fe-IZO nanocomposite (NC) was synthesized via the facile co-precipitation method. The Fe-IZO NC was characterized by various experimental techniques and the results attained allowed us to suggest the mechanism for considering the connection between morphological variations and gas-sensing characteristics of Fe-IZO NC. One of the cutting-edge circumstances now-a-days is the prime use of gas sensors for environmental applications. The NC has a good selectivity for NH3 gas that has shown good stability up to 80 d. The response time (ℸres) is 64 s, recovery time (ℸrec) is 72 s for 50 ppm (parts per million) and obtained a highest response of 87% at room temperature. The Fe-IZO NC heterostructure with efficient charge transfer can enable the subsequent reaction mechanism with NH3 gas and the adsorbed oxygen ions leading to an increased response. Consequently, the results attained in this work has shown that the synthesized heterostructured NC sensor is suitable for room temperature NH3 sensing with high performance.
3D Printed Rechargeable Aqueous and Non-Aqueous Lithium-Ion Batteries: Evolution of Design and Performance
Authors: Egorov Vladimir , Gulzar Umair , O’Dwyer Colm
Published: December 2023

Here we describe the modeling and design evolution of vat polimerized (Vat-P) stereolithographic apparatus (SLA) 3D printed coin cell-type aqueous and non-aqueous rechargeable lithium-ion batteries, cases and current collectors. We detail the rationale for design evolution that improved performance, handling and assembly of the printed batteries. Some guidance into the modeling, 3D printing process, material choice, chemical and electrochemical stability, assembly, sealing, and performance of 3D printed Li-ion batteries is outlined. 3D printed Li-ion batteries demonstrated promising results in terms of gravimetric capacity, rate capability, and capacity per unit footprint area compared to conventional coin cells in both aqueous and non-aqueous systems. For aqueous cells, the cell level capacity is a factor of 2–3x higher than similar metal coin cells due to the lighter weight and better rate response. We also outline design requirements for a Vat-P printed battery that are compatible with organic carbonate-based electrolytes, where the cell provides 115 mAh g−1 specific capacity using an LiCoO2–graphite chemistry, which is only ∼20% less than the maximum reversible capacity of LCO. Despite the challenges faced in optimizing the design and materials for 3D printed Li-ion batteries, this study provides valuable information for future research and development.
An Improved Big Data Analytics Architecture for Intruder Classification Using Machine Learning
Authors: Babar Muhammad , Kaleem Sarah , Sohail Adnan , Asim Muhammad , Tariq Muhammad Usman
Published: December 2023

The approval of retrieving information on the Internet originates several network securities matters. Intrusion recognition is a critical study in network security to spot unauthorized admission or occurrences on protected networks. Intrusion detection has a fully-fledged reputation in the current era. Research emphasizes several datasets to upsurge system precision and lessen the false-positive proportion. This article proposes a new intrusion detection system using big data analytics and deep learning to address some of the misuse and irregularity detection limitations. The proposed method could identify any odd activities in a network to recognize malicious or unauthorized action and permit a response during a confidentiality break. The proposed system utilizes the big data analytics platform based on parallel and distributed mechanisms. The parallel and distributed platforms improve the training time along with the accuracy. The experimentation appropriately classifies the information as either normal or abnormal. The proposed system has a recognition proportion of 96.11% that pointedly expands overall recognition accuracy related to existing strategies.
Real-Life Effectiveness and Tolerability of Brivaracetam in Focal to Bilateral and Primary Generalized Tonic-Clonic Seizures
Authors: Fonseca Elena , Gifreu Ariadna , Abril Jaramillo Javier , Redondo Vergé Luis , Quintana Manuel , Lallana Sofía , López-Maza Samuel , Abraira Laura , Campos-Fernández Daniel , Santamarina Estevo , Rodríguez Uranga Juan , Toledo Manuel
Published: December 2023

Purpose. Brivaracetam (BRV), an antiseizure medication indicated for focal-onset seizures, has shown efficacy in the treatment of focal to bilateral tonic-clonic seizures (FBTCS). We aimed to determine the effectiveness and safety of BRV in patients with FBTCS and generalized tonic-clonic seizures (GTCS). Methods. We performed a multicenter, retrospective, longitudinal study in adult patients with epilepsy who experienced at least one FBTCS or GTCS before starting BRV (baseline visit). Data were collected from consecutive outpatient visits over a 4-year period. All patients had been followed for at least 3 months before the baseline visit and completed a minimum follow-up of 3 months after starting BRV. Response (≥50% reduction in FBTCS/GTCS frequency) and retention rates, as well as seizure freedom and presence of adverse events at 3, 6, and 12 months, were recorded as outcome measures. Results. 114 patients were included (mean age

36.3
±
18.0

years, 52% male, 36.6% genetic generalized epilepsy); 94 had a 12-month follow-up period. At 12 months’ follow-up, the response rate was 83%, and 73.4% of patients were FBTCS/GTCS-free. Retention was 79% at 12 months. Adverse events occurred in 29.8% of patients, the most common being drowsiness (14.9%). No significant differences were found in response rates between FBTCS and GTCS. Drug resistance was independently associated with lower response and seizure freedom rates at follow-up. The absence of a titration period predicted seizure freedom and response at 3 months. Conclusions. BRV is an effective and well-tolerated treatment in patients with focal to bilateral tonic-clonic seizures and generalized tonic-clonic seizures.
Targeted Imaging of Endometriosis and Image-Guided Resection of Lesions Using Gonadotropin-Releasing Hormone Analogue-Modified Indocyanine Green
Authors: Peng Jing , Liu Qiyu , Pu Tao , Zhang Mingxing , Zhang Meng , Du Ming , Li Guiling , Zhang Xiaoyan , Xu Congjian
Published: December 2023

Objective. In this study, we utilized gonadotropin-releasing hormone analogue-modified indocyanine green (GnRHa-ICG) to improve the accuracy of intraoperative recognition and resection of endometriotic lesions. Methods. Gonadotropin-releasing hormone receptor (GnRHR) expression was detected in endometriosis tissues and cell lines via immunohistochemistry and western blotting. The in vitro binding capacities of GnRHa, GnRHa-ICG, and ICG were determined using fluorescence microscopy and flow cytometry. In vivo imaging was performed in mouse models of endometriosis using a near-infrared fluorescence (NIRF) imaging system and fluorescence navigation system. The ex vivo binding capacity was determined using confocal fluorescence microscopy. Results. GnRHa-ICG exhibited a significantly stronger binding capacity to endometriotic cells and tissues than ICG. In mice with endometriosis, GnRHa-ICG specifically imaged endometriotic tissues (EMTs) after intraperitoneal administration, whereas ICG exhibited signals in the intestine. GnRHa-ICG showed the highest fluorescence signals in the EMTs at 2 h and a good signal-to-noise ratio at 48 h postadministration. Compared with traditional surgery under white light, targeted NIRF imaging-guided surgery completely resected endometriotic lesions with a sensitivity of 97.3% and specificity of 77.8%. No obvious toxicity was observed in routine blood tests, serum biochemicals, or histopathology in mice. Conclusions. GnRHa-ICG specifically recognized and localized endometriotic lesions and guided complete resection of lesions with high accuracy.
Prediction of Crop Yield by Support Vector Machine Coupled with Deep Learning Algorithm Procedures in Lower Kulfo Watershed of Ethiopia
Authors: Ayalew Abebe Temesgen , Lohani Tarun Kumar
Published: December 2023

Sensible and judicious utilization of water for agriculture in conjunction with prediction techniques increases the crop yield. The Ethiopian economy relies on and is exclusively dependent on agricultural-based activities. Different soil compositions (nitrogen, phosphorous, and potassium), crop alternation, soil dampness, and climate conditions play an imperative contribution in cultivation. The primary purpose of this study was to conduct a machine learning approach which can be practiced dynamically for efficient farming at a low cost. The support vector machine (SVM) was applied as a machine learning procedure, whereas long short-term memory (LSTM) and the recurrent neural network (RNN) were considered as deep learning procedures. The research comprised a model that is combined with machine learning procedures (ANN, random forest, and decision tree) to know efficient and appropriate crop types. The planned model is improved through conducting deep learning methods incorporated to the existing practice for different crop condition. Pure data and related evidence are attained concerning the quantities of soil constituents desired through their expenditures distinctly. It delivers well precision as compared to the current model examining the specified documents and assisting the local agronomists in forecasting different types of crop and gain benefits. In RNN, LSTM, and SVM algorithms, the accuracy is determined as 96% which is comparatively preferable as compared to other machine learning procedures under different feature and crop types. The techniques are evaluated in terms of percentage in prediction accuracy. The results generated are important for agrarians, experts, researchers, and local farmers to maximize the crop productivity and help to enhance agriculture and climate change-related decisions, especially in low-to-middle-income countries.
Group Action on the Set of Nonunits in Rings
Authors: Almotairi Eman S. , Bhat M. Imran , Alghamdi Ahmad M.
Published: December 2023

Let

R

be a ring,

G

be the group of all units of

R

, and

X
=
R
−
G
∪

0

. In this paper, we investigate

a
v

x

x

∈
X

=

o

x

x

∈
X

for a ring

R

, where

a
v

x

is the set of all vertices of the zero-divisor graph of

R

adjacent to

x

. We also investigate the question on zero-divisor graphs posed in the literature such that when the equality

o

1
−
e

=
a
v

e

holds in a commutative regular ring

R

with identity. Here,

e

is a nonzero idempotent of

R

which is not the identity element of

R

.
Supplementary Figure 2 from Phase Ib Trial of Phenformin in Patients with V600-mutated Melanoma Receiving Dabrafenib and Trametinib
Authors: Chapman Paul B. , Klang Mark , Postow Michael A. , Shoushtari Alexander Noor , Sullivan Ryan J. , Wolchok Jedd D. , Merghoub Taha , Budhu Sadna , Wong Phillip , Callahan Margaret K. , Zheng Bin , Zippin Jonathan
Published: December 2023

Supplementary Figure 2. Fold change in body weight of patients by dose level.
FIGURE 2 from Phase Ib Trial of Phenformin in Patients with V600-mutated Melanoma Receiving Dabrafenib and Trametinib
Authors: Chapman Paul B. , Klang Mark , Postow Michael A. , Shoushtari Alexander Noor , Sullivan Ryan J. , Wolchok Jedd D. , Merghoub Taha , Budhu Sadna , Wong Phillip , Callahan Margaret K. , Zheng Bin , Zippin Jonathan
Published: December 2023

Pharmacokinetics of phenformin. Pharmacokinetic results are shown for the 50 mg bid cohort (A), 100 mg bid cohort (B), 150 mg bid cohort (C), and the 200 mg bid cohort (D). Data for patients who missed a substantial number of doses due to toxicity are not shown. For the patients in the 200 mg bid cohort (D), red bars indicate days on which phenformin was held due to toxicity. The arrows indicate dose reductions. E, Box and whiskers plot of day 7 phenformin plasma concentrations by dose level. The mean is indicated by the horizontal lines. Each box indicates 25%–75% quartiles; whiskers indicate the range. For the 200 mg dose level, there were data on only 2 patients and so no box is shown. bid, twice daily.
The Study on The Relationship Between Promotional Strategies to Freshmen Students’ Perceptions and Needs in A Higher Educational Institution in Iligan City
Authors: Guro Princess Diamond B. , Iman Jimm M. , Pendang Ma. Lynflora , Madura Rhodo Feliah , Abiol Michael Jere’ D.
Published: December 2023

This study delves into the impact of promotional strategies on the perceptions and needs of freshmen students in higher education institutions. Its primary objective is to scrutinize the connection between promotional strategies, customer perception, and customer needs, with the goal of formulating precise and efficient marketing tactics. Conducted at a higher education institution in Iligan City, Philippines, the research encompasses participants from various colleges, ensuring a well-rounded sample. Employing a descriptive-correlational research design, data was gathered through an online survey questionnaire and subjected to analysis through descriptive statistics and Pearson correlataion analysis. The outcomes reveal a substantial positive correlation between promotional strategies and both customer perception and customer needs. Promotional strategies that are personalized, convenient, and sustainable are shown to enhance customer experience, customer value, and brand loyalty. This study offers valuable insights for both marketers and scholars, advocating for educational institutions to implement marketing programs with a focus on promotional strategies. Future research should explore the efficacy of promotional strategies across different contexts and diverse demographic groups to ensure all-encompassing and impactful marketing strategies. By comprehending the influence of promotional strategies on customer perception and needs, businesses and educational institutions can craft tailored marketing campaigns to attract and retain customers in today's fiercely competitive market landscape.
Perceived Impact of Modern-Day Inflation on the Budget Consumption and Decisions of Senior High School Learners
Authors: Lastimosa Robert Julius , Hornada Jasmine Kate , Sedik Ayessa Leigh B. , Gaygay Jr. Carlos Friales
Published: December 2023

nflation has affected numerous aspects of living globally, and this consistent drop in purchasing power has been attributed to the rise of overall prices of goods, leading to greater challenges in global living standards. There have been numerous studies indicating the impact of inflation amongst many stakeholders, but in this study, the objective is to assess whether it also affected the learners of a selected institution in the same way. Hence, a descriptive-correlational design was utilized to describe and correlate the levels of perceived assessment on modern-day inflation and the perceived changes in the budget consumption and decisions of Senior High School learners in the period of 2022. A structured Likert scale survey questionnaire adopted from the study of Avila & Gatpolintan (2019) was distributed to a sample of 282 respondents using simple random sampling. The data were analyzed using descriptive statistics for the first two objectives and Pearson r correlation for the third. The study revealed a very high level of agreement in the price increase brought by their perceptions toward modern-day inflation, a very high level of agreement on their perceived changes toward expenditures, and a high level for their preferences and quantity of consumption. The study also noted a moderate positive correlation (p<0.05) between the two variables, indicating that the learners' budgeting behaviors are influenced by their assessment of rising prices and that their perceptions have also been influenced by how they chose and quantified their necessities, respectively.
The Effect of Financial Literacy, Locus of Control and Life Style on the Financial Behavior of Peer to Peer Lending Paylater User In Surabaya City
Authors: Berlianti Selvina Novantia , Suwaidi Rahman Amrullah
Published: December 2023

Paylater is the latest payment system issued by e-commerce that we can shop for anything and paylater (buy now pay later). However, this convenience makes people more consumptive. This study aimed to determine how financial literacy, locus of control, and lifestyle factors influence the financial behavior of Paylater users in Surabaya. The sample will consist of 100 respondents who will be chosen using the Cochran method and a purposive sampling technique. The data analysis method applied is Partial Least Square (PLS). The results of this study indicate that financial literacy, locus of control, and lifestyle all play a role in influencing financial behavior.
Digital Marketing Strategy for Medical Supplier Company in Covid-19 Pandemic Era
Authors: Mona Nailul , Audini Yassmine
Published: December 2023

In the covid-19 global pandemic era, many countries apply restriction for many offline activities in order to prevent virus spread. This affected many business and force them to go online. Every business have to adapt and adopt technology within this pandemic situation. One of the business whose product demand are increasing, is a medical supplier company. But this increase not only in the matter of demand, but also competitor and product variation. Existing company should run digital transformation to secure and expand their position on the market. This paper explain digital marketing communication strategies which conducted by medical supplier company in covid-19 pandemic era. The company make use of some platform for the initial digital marketing strategy, such as social media activation, company website, and e-commerce. Result shows that digital marketing plays important role for product sales escalation. Company can promote their business and establish customer relationship through various digital channel. However, e-commerce strategy seems more challenging for medical supplier company. This paper could help other business to determine their suitable digital marketing communication strategy.
Optimalisasi Penerapan Metode ARIMA dalam Mengestimasi Harga Emas di Negara Indonesia
Authors: Panjaitan Adne Sagita , Maretha M Rafly , Hilmiah  , Mardhotillah Bunga
Published: December 2023

Tahun 2023 Indonesia tercatat sebagai negara pemilik cadangan emas terbesar nomor 6 di dunia. Dan menurut data yang diberikan Ditjen Minerba Kementerian ESDM pada tahun 2021 Indonesia menghasilkan 118,3 ton emas murni, menjadikan negara Indonesia sebagai penghasil emas nomor 9 di dunia. Keberadaan Indonesia dalam cincin api pasifik ditandai dengan aktivitas vulkanik akibat pergerakan lempengan bumi yang menghasilkan pergerakan tektonik di bawah permukaan bumi menjadikan potensi adanya pertambang di Indonesia sangat besar. Dimana menurut Kementerian Energi dan Sumber Daya Mineral (ESDM) Indonesia memiliki sekitar 1.182.073,53 hektare tambang emas yang tersebar ke dalam 25 provinsi.  Emas sendiri merupakan salah satu alat tukar menukar. Dimana menurut web www.goldprice.org di Indonesia pada bulan September 2022 sampai pada September 2023 mengalami peningkatan sebesar 12% namun pada bulan januari 2023 harga emas mengalami penurunan sebesar 7%. Adanya penurunan harga ini dapat mempengaruhi investor yang ingin menginvestasikan harta mereka ke emas. Maka dari ini penulis tertarik untuk menerapkan estimasi Autoregressive Integrated Moving Average (ARIMA) dalam menghitung harga emas penulis memilih negara Indonesia sebagai tempat melakukan penelitian, dimana Indonesia merupakan salah satu penghasil emas terbesar ke 6 di dunia.  Dengan ini penulis berasumsi diperlukan adanya penelitian dengan pemodelan matematika untuk menganalisis, memprediksi, dan mengidentifikasi estimasi harga emas kedepannya. Adapun pemodelan matematika yang penulis pilih untuk menganalisis, memprediksi dan mengidentifikasi estimasi harga emas kedepannya di negara Indonesia adalah pendekatan deskriptif kuantitatif dengan menerapkan metode Autoregressive Integrated Moving Average (ARIMA) dalam perhitungannya. Dimana tujuan penulis sendiri melakukan penelitian adalah untuk mewujudkan SDGs dalam sektor ekonomi, yang harapannya dengan adanya penelitian ini dapat membawa banyak pengaruh untuk kedepannya terutama untuk negara Indonesia sendiri.
Why are single-use plastics a problem?
Authors:   Faculdade de Tecnologia de Jacareí, Pagotto Érico Luciano , Paiva Daniel Felipe de ,   Moeté Filmes, Gonçalves-Dias Sylmara Lopes Francelino ,   Universidade de São Paulo. Escola de Artes, Ciências e Humanidades, Vallin Isabella de Carvalho ,   Universidade de São Paulo. Escola de Artes, Ciências e Humanidades, Carvalho Isabela Ribeiro Borges de ,   Universidade de São Paulo. Instituto de Energia e Ambiente
Published: December 2023

Given the importance of broadening the debate on plastic pollution outside the academic environment, to include waste pickers, users of basic sanitation systems, elementary school students, teachers and civil society as a whole, volume IV of the collection brings pedagogical activities with the aim of drawing public attention to the problem and contributing to the construction of a more critical view of the complex issue of plastics. This book is part of "NOSS Repensando o Plástico" collection.
Linguistic (and Ontological?) encounters between Plato and Karl Popper
Authors: Christos Terezis
Published: December 2023

In this study, I attempt to shed light on whether some passages from the Platonic dialogue Cratylus that deal with language correspond to Karl Popper’s theory on the third world. Specifically, I attempt to prove that Plato’s third world contains both divine and human properties, something that is provided through language, that is, through the human rational and developing in objective terms construction. In the four subchapters of my study, I basically investigate the relationship between the thinking subject and the noumenon as well as the role of the coiner of names and the dialectician with respect to the scientific foundation of the names. The most important conclusion drawn is that according to Plato these two are responsible for connecting a thinking subject with a noumenon by adding objective certainty to the meaning of everything is said. I also investigate how Plato’s archetypal Ideas could be considered to be the third world.
Historic house museums and their change of use: searching for balanced building performance: a case study in São Paulo, Brazil
Authors: Cansado Júlia Ascencio , Ornstein Sheila Walbe
Published: December 2023

A house museum has its own particularities, presenting many challenges in terms of conservation of the building itself and its surroundings. This article aims to present integrative methods that can be adopted by museum managers and teams to monitor the building performance of a historic house museum. An exploratory study was carried out using Post-Occupancy Evaluation (POE), a multi-methods non-invasive application, at the Ema Klabin historic house museum in São Paulo, Brazil. This approach analyses qualitative and quantitative aspects of the environments to evaluate their performance and the context in which they operate, including the point of view aired by researchers/evaluators, other experts, and regular users. Summary charts and maps regarding diagnosis and recommendations were developed to demonstrate the results achieved with these methods. They are the result of cross-sectional surveys, providing input to aid decision making and developing possible guidelines for future projects with similar characteristics.
A invenção do Brasil pós-Brasília: história de homens brancos
Authors: Bragaia Flavio Antonio Dugo
Published: December 2023

Ao nos debruçarmos sobre as edições das revistas Acrópole, Habitat e Módulo, publicadas entre a inauguração de Brasília e o Golpe Militar de 1964, nos damos conta de que, além das referências teóricas e projetuais, a leitura feita por arquitetos e urbanistas sobre seu objeto de trabalho informa e é informada por uma determinada maneira de contar a história do Brasil, e de encarar questões relacionadas à cultura, política e identidade, que conserva parte de sua estrutura até hoje. Nesse sentido, este artigo aborda três diferentes abordagens historiográficas, as quais, ao longo do período estudado, encontraram espaços de destaque nas revistas mencionadas. Nosso objetivo é examinar os termos empregados por aqueles que, durante um momento de intensa movimentação política, econômica e social no Brasil, procuravam escrever e reescrever a história do país.
El “espíritu libre” y Goliadkin: una lectura de Dostoievski bajo la crítica nietzscheana
Authors: Bombachi Tomás Salvador
Published: December 2023

En el presente trabajo se propone relacionar la novela El doble (1848) de F.M. Dostoievski con algunos conceptos fundamentales esbozados por F. Nietzsche en Humano, demasiado humano (1886). Pensamos que los héroes dostoievskianos presentan rasgos que Nietzsche conceptualizará (para remarcar un poco la posterioridad de Friederich) como “espíritus libres”, y luego “caminantes”. El propósito del escrito será estudiar y determinar si, además de la implícita relación entre ambos escritores/pensadores, es posible concebir a Goliadkin dentro de la formulación “criticismo dostoievskiano” y, necesariamente, como “espíritu libre”: este último entendido como aquellos que critican lo heredado y martillan las cadenas morales de la sociedad.
Russian Civic Criticism and the Idyllic Dream in Ivan Goncharov’s "Oblomov"
Authors: Oliveira Cassio de
Published: December 2023

Nikolai Dobroliubov’s and Dmitrii Pisarev’s reviews of Ivan Goncharov’s novel Oblomov have gone into history as exemplars of Russian civic criticism. Their main argument centers on the eponymous protagonist’s seeming inability to exit his lethargic condition, which they interpret as a symptom of the Russian status quo at the time of the Great Reforms. In the present article, I argue that the case of Oblomov demonstrates the limits of the civics’ mimetic criticism. The dominant chronotope of the novel, namely the idyll, indicates that Oblomov is not in essence a novel about the hero’s inability to change (which would presuppose a willingness to, or desire for, said change), but rather about his longing for a restorative past which is ultimately inaccessible to him.
A República do Cruzeiro do Sul
Authors: Briúsov Valiéri , Zorattini Júlia
Published: December 2023

Escrito por Valiéri Briúsov (1873-1924) e publicado em 1907, “A República do Cruzeiro do Sul” é um conto que descreve a devastação de uma cidade futurista (à primeira vista, uma utopia industrial) erguida sobre o Polo Sul, em virtude da epidemia de uma doença psíquica. Mesmo antes do desencadeamento da tragédia, a Cidade Estelar dificilmente poderia ser considerada uma sociedade ideal: as vidas de seus habitantes eram controladas nos mínimos detalhes por um despótico Conselho formado pelos diretores fabris locais. Trata-se, portanto, de uma narrativa distópica, antecessora de obras mais conhecidas do gênero, como Nós (1924), de Evguêni Zamiátin, e 1984 (1949), de George Orwell, e pioneira da longa e rica tradição da literatura de expressão russa de ficção científica. Briúsov, um dos expoentes do simbolismo russo, surpreende ao fazer uso de um estilo enxuto, mais adequado ao formato jornalístico em que é veiculado o conto. A descrição sintética e até mesmo brusca das atrocidades que se passaram na capital da República apenas intensifica a aversão sentido pelo leitor. Buscamos manter essa precisão na tradução[1]. Além desta versão, o conto também foi traduzido para as línguas inglesa, francesa, alemã, italiana e, mais recentemente, espanhola, tradução esta publicada na edição nº 5 da revista argentina Eslavia em 2020 e realizada por Eugenio López Arriazu.

 

[1] Agradecemos as sugestões e comentários da professora Ekaterina Vólkova Américo.
Poetas russos em português
Authors: Pereléchin Valéri , Bonavina Rafael
Published: December 2023

Tradução para o português brasileiro da resenha “Poetas russos em português” (Russkie poéty po-portugalski), de Valeri Pereléchin, com uma breve apresentação crítica ao texto.
“Dando voltas para frente”: teoria e crítica hoje
Authors: Durão Fabio Akcelrud , Marques Priscila Nascimento , Nascimento Rodrigo Alves do
Published: December 2023

Entrevista sobre teoria e crítica literárias hoje com Fabio Akcelrud Durão, professor titular de Teoria da Literatura da Universidade Estadual de Campinas, autor, dentre outros livros, de O que é Crítica Literária (Nanquim/Parábola, 2016), Ensinando Literatura: a sala de aula como acontecimento (Parábola, 2022), Metodologia de pesquisa em literatura (Parábola, 2020) e Teoria (literária) americana (Autores Associados, 2011).
A crítica nos estudos russos: entre teoria e prática social
Authors: Nascimento Rodrigo , Marques Priscila Nascimento
Published: December 2023

Apresentação Dossiê.
Efficacy of neurofeedback training for improving attentional performance in healthy adults: A systematic review and meta-analysis
Authors: Kimura Ikko , Noyama Hiroki , Onagawa Ryoji , Takemi Mitsuaki , Osu Rieko , Kawahara Jun-ichiro
Published: December 2023

This systematic review and meta-analysis examined the neurofeedback training (NFT) effects on attentional performance in healthy adults. Six databases were searched until June 2022 to identify parallel randomized controlled trials (RCTs) evaluating attentional improvements after NFT. Risk of bias was assessed using the Cochrane Collaboration tool. We identified 41 RCTs for qualitative synthesis and 15 RCTs (569 participants) for meta-analysis. The overall NFT effect on attentional performance was significant (standardized mean difference = 0.27, 95% confidence interval = 0.10–0.44). However, no significant pooled effect was found within the trials comparing its effect with sham-NFT (eight RCTs). Additionally, variable effects were observed on individual subsets of attentional performance. Further sham-controlled RCTs are required to validate the improvement of attentional performance with NFT.
Opioid Use Disorder in Patients Undergoing Major Lower Extremity Amputation: Prevalence and Outcomes
Authors: Pitsenbarger Luke T. , Chao Natalie T. , Karwoski Allison S. , Som Maria N. , Workneh Eyerusalem N. , Dunlap Nora , Fitzpatrick Suzanna Simmonds , Nagarsheth Khanjan H.
Published: December 2023

Introduction

Patients with a history of Opioid Use Disorder (OUD) have higher postoperative complication rates and mortality in many settings. Yet, it remains poorly understood how the opioid epidemic has affected patients undergoing major lower extremity amputation (LEA) and whether outcomes differ by OUD status.

Methods

We conducted a retrospective chart review of all 689 patients who underwent major LEA at a large tertiary referral center from 2015 to 2021. This study assessed patient characteristics and long-term postoperative outcomes for patients with preoperative OUD.

Results

133 (19.3%) patients had a lifetime history of preoperative OUD. Preoperative OUD was associated with key characteristics, comorbidities, and outcome measures. OUD was significantly associated with younger age ( P < .001), black race ( P = .026), single relationship status ( P < .001), BMI <30 ( P = .024), no primary care provider ( P = .004), and Medicaid insurance ( P < .001). Comorbidities significantly associated with OUD include current smoking ( P < .001), Human Immunodeficiency Virus (HIV; P = .003), and history of osteomyelitis ( P < .001). Preoperative OUD independently predicted lower rates of 30-60-day readmission (odds ratio [OR] .54, P = .018) and 1-12-month reamputation (OR .41, P = .006). There was no significant difference in long-term mortality and follow-up.

Conclusion

This study demonstrates the prevalence of OUD in patients undergoing major LEA and reports associations and long-term outcomes. Our findings highlight the importance of recognizing OUD and raise questions about the mechanisms underlying its relation to rates of postoperative readmission and reamputation.