bluuebunny/crossref_metadata_2025 huggingface dataset:
PENGARUH MAKRO EKONOMI TERHADAP HARGA SAHAM PT. INDOFOOD CBP SUKSES MAKMUR TBK DI KOTA MAKASSAR
Authors: Saputra Arman , Abduh Thamrin , Said Miah
Published: December 2023

Penelitian ini bertujuan untuk mengetahui pengaruh variabel makro ekonomi terhadap harga saham PT Indofood CBP Sukses Makmur Tbk. di Kota Makassar baik secara simultan maupun secara parsial. Penelitian ini menggunakan pendekatan kuantitatif yang mengkaji Pengaruh Variabel Makro Ekonomi terhadap Harga Saham PT Indofood CBP Sukses Makmur Tbk. Di Kota Makassar. Teknik pengumpulan data melalui dokumentasi. Teknik analisis data menggunakan teknik kuantitatif dengan pendekatan statistik yaitu analisis regresi berganda. Hasil penelitian Berdasarkan hasil analisis regresi linear berganda diperoleh persamaan regresi linear berganda sebagai berikut. Dari persamaan regresi berganda di atas dapat dilihat bahwa inflasi koefisien bernilai positif artinya terjadi hubungan. Komponen variabel makro ekonomi yang terdiri dari inflasi dan nilai tukar terhadap secara simultan berpengaruh signifikan terhadap harga saham. makro ekonomi yang terdiri dari inflasi dan nilai tukar secara parsial tidak berpengaruh signifikan terhadap harga saham dan inflasi secara parsial berpengaruh signifikan terhadap harga saham, sehingga yang lebih dominan berpengaruh terhadap harga saham adalah inflasi. Berdasarkan hasil analisis korelasi, maka variabel makro ekonomi yang terdiri dari inflasi dan nilai tukar berpengaruh sedang terhadap harga saham.
This study aims to determine the effect of macroeconomic variables on the stock price of PT Indofood CBP Sukses Makmur Tbk. In Makassar City, either simultaneously or partially. This study uses a quantitative approach that examines the Effect of Macroeconomic Variables on the Share Price of PT Indofood CBP Sukses Makmur Tbk. In Makassar City. Data collection techniques through documentation. Data analysis techniques using quantitative techniques with a statistical approach, namely multiple regression analysis. Research results: The following multiple linear regression equations are obtained Based on the results of multiple linear regression analysis. From the multiple regression equation above, it can be seen that the inflation coefficient is positive, meaning there is a relationship. Components of macroeconomic variables consisting of inflation and exchange rates simultaneously significantly affect stock prices. Macroeconomics consisting of inflation and exchange rates partially have no significant effect on stock prices, and inflation partially has a significant effect on stock prices, so inflation is more dominant in influencing stock prices. Based on the results of the correlation analysis, the macroeconomic variables consisting of inflation and exchange rates have a moderate effect on stock prices.
Nilai Dan Manfaat Ekonomi Keberadaan Area Lego-Lego Di Kawasan Center Point Of Indonesia Sebagai Salah Satu Bentuk Pemanfaatan Ruang Terbuka Hijau
Authors: Dwiyanti Fany Ulfa , Latief Rudi , Saleh Haeruddin
Published: December 2023

Tujuan penelitian adalah untuk menganalisis nilai fungsi pelayanan, nilai ekonomi, dan besarnya manfaat ekonomi bagi masyarakat dengan keberadaan ruang terbuka hijau Area Lego-Lego di Kawasan Center Point of Indonesia Kota Makassar. Metode penelitian yang digunakan adalah metode pendekatan kuantitatif dan kualitatif yang diinterprestasikan secara deskriptif kuantitatif yaitu dengan mengumpulkan, mengolah, menyajikan dan menjabarkan hasil penelitian penelitian. Adapun Metode biaya pengganti (replacement cost) dan CVM (WTP) dan analisis deskriptif kuantitatif berdasarkan perubahan pendapatan dengan dan tanpa adanya Area Lego-Lego. Hasil peneltian yang diperoleh bahwa keberadaan Keberadaan Area Lego-Lego Di Kawasan Center Point of Indonesia memberikan pengaruh yang positif terhadap perubahan pendapatan sebagai masyarakat dan menurut pengunjung keberadaan Keberadaan Area Lego-Lego Di Kawasan Center Point of Indonesia mampu menjadi salah satu pilihan sebagai tempat bersantai setelah bekerja. Pemerintah perlu mengupayakan agar target penambahan Ruang Terbuka Hijau khususnya berupa taman Kota dapat terwujud, serta pemeliharaan fasilitas yang ada juga perlu ditingkatkan. Taman merupakan area publik siapapun harus ikut berpartisipasi dalam menjaga keberlangsungannya.
The purpose of the study was to analyze the value of the service function, economic value, and the magnitude of the economic benefits for the community with the existence of green open spaces in the Lego-Lego Area in the Center Point of Indonesia area of Makassar City. The research method used is a quantitative and qualitative approach which is interpreted quantitatively descriptively by collecting, processing, presenting and describing the results of research studies. The method of replacement cost and CVM (WTP) and quantitative descriptive analysis based on changes in income with and without the Lego-Lego Area. The results of the research show that the existence of the Lego-Lego Area in the Center Point of Indonesia has a positive influence on changes in income as a community and according to visitors the existence of the Lego-Lego Area in the Center Point of Indonesia can be an option as a place to relax after work. The government needs to make efforts to achieve the target of adding green open spaces, especially in the form of city parks, and the maintenance of existing facilities also needs to be improved. Parks are public areas, everyone must participate in maintaining its sustainability.
Strategi Peningkatan Kualitas Permukiman Kumuh Di Kelurahan Tallo Kecamatan Tallo Kota Makassar
Authors: Mardjuni St. Nurhalizah , Manaf Murshal , Latief Rudi
Published: December 2023

Penelitian ini sebagai acuan untuk meningkatkan kualitas permukiman kumuh di Kelurahan Tallo. Judul Strategi Peningkatan Kualitas Permukiman Kumuh dengan fokus studi Peningkatan Kualitas Permukiman Kumuh. Berdasarkan SK Kumuh, Kelurahan Tallo masuk dalam kawasan yang ditetapkan sebagai kawasan kumuh, hal ini disebabkan oleh beberapa faktor penyebab tumbuh kembangnya permukiman kumuh di lokasi ini. Dalam Penelitian ini bertujuan untuk mengetahui peningkatan kualitas permukiman kumuh berpengaruh terhadap faktor penyebab tumbuh kembangnya permukiman kumuh di Kelurahan Tallo Kecamatan Tallo Kota Makassar dan untuk merumuskan strategi peningkatan kualitas permukiman kumuh di Kelurahan Tallo Kecamatan Tallo Kota Makassar. Adapun metode penelitian ini dilakukan dengan menggunakan analsisi diantaranya Analisis Chi-Square digunakan untuk dua variabel adalah independen berarti tidak memiliki hubungan, dan oleh karena itu yang tidak bergantung pada yang lain, begitu pula sebaliknya. Dan analisis SWOT digunakan untuk merumuskan strategi peningkatan kualitas permukiman kumuh.
This research is a reference to improving the quality of settlement slums in Tallo Village. The Title of the Settlement Quality Improvement Strategy Slums, with the focus of the study on improving the quality of slums. Based on the SK Kumuh, Tallo Village is included in the designated area as a slum area. This is caused by several factors, including the development of slum settlements in this location.This study aims to determine how the improvement of the quality of slum settlements affects the factors that cause growth and development of slum settlements in Tallo Village, Tallo District, Makassar City, and to formulate a strategy to improve the quality of slum settlements in Tallo Village, Tallo District, Makassar City. The research method is carried out by using analysis, including Chi-Square Analysis, for two independent variables, meaning they have no relationship and therefore are independent of one another. SWOT analysis is used to formulate strategies to improve the quality of slum settlements.
Benchmarking of Hardware-efficient Real-time Neural Decoding in Brain-computer Interfaces
Authors: Hueber Paul , Tang Guangzhi , Sifalakis Manolis , Liaw Hua-Peng , Micheli Aurora , Tömen Nergis , Liu Yao-Hong
Published: December 2023

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency.

Previous benchmarks have provided limited insights into energy efficiency and latency. This paper, however, introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex, and explores the suitability for low latency and low compute neural decoding. The study finds that ANN-based decoders provide superior decoding accuracy, requiring a high latency and many operations to decode neural signals effectively. Spiking neural networks emerge as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of the computational resources.

These distinctive advantages of neuromorphic spiking neural networks, positions them as highly suitable for the challenging environment of closed-loop neural modulation. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intracortical human-machine interaction.
Subjective Bayesian Network-based Interdependent Mission Impact Assessment with Game-Theoretic Attack-Defense Interactions
Authors: Yoon Han Jun , Thukkaraju Ashrith Reddy , Matsumoto Shou , Ferrari Jair Feldens , Lee Dongwhan , Ahn Myung Kil , Costa Paulo , Cho Jin-Hee
Published: December 2023

An accurate assessment of a mission system's performance, called Mission Impact Assessment (MIA), can effectively identify and counteract any issues in the current system to avoid potential risks or vulnerabilities that may cause mission failure. Although the importance of MIA research has been recognized to mitigate the system's potential risk for more than a decade, little research has shown a comprehensive MIA framework with experimental validation showing the inference performance of the MIA tools under realistic attack-defense interactions. To fill this gap, we propose an interdependent mission impact assessment (MIA), called iMIA, that can adequately capture the inter-relationships of the key components in a mission system and environment and accurately infer a mission outcome (i.e., success or failure) under uncertainty.  In the proposed iMIA, we first consider Subjective Bayesian Networks to evaluate how successfully a given system introduces impacts on mission success under uncertainty due to a lack of evidence.  In addition, we consider strategic attack-defense interactions based on a hypergame theory in which the attacker and defender can take actions under their perceived uncertainty.  Our extensive experiments showed the outperformance of our proposed MIA up to 70% and 25% over the performance of the baseline and the state-of-the-art Bayesian Network-based counterparts, respectively, in terms of inference accuracy in mission performance (e.g., mission outcomes, such as mission success or failure).  We also provide in-depth sensitivity analyses to identify the key system components that should be considered more carefully to avoid mission failure.
Temporal Early Exits for Efficient Video Object Detection
Authors: Sabet Amin , Xun Lei , Hare Jonathon , Al-Hashimi Bashir M. , Merrett Geoff V.
Published: December 2023

Efficiently transferring image-based object detectors to the domain of video remains challenging under resource constraints. Previous efforts used feature propagation to avoid recomputing unchanged features. However, the overhead is significant when working with very slowly changing scenes, such as in surveillance applications. In this paper, we propose temporal early exits to reduce the computational complexity of video object detection. Multiple temporal early exit modules with low computational overhead are inserted at early layers of the backbone network to identify the semantic differences between consecutive frames. Full computation is only required if the frame is identified as having a semantic change to previous frames; otherwise, detection results from previous frames are reused. Experiments on ImangeNet VID and TVnet show that the approach can accelerate video object detection by 1.7x compared to SOTA, with a reduction of only <1% in mAP.
Toxic Comment Detection Using Bidirectional Sequence Classifiers
Authors: Maity Amit , More Rishi , Patil Abhijit , Oza Jay , Kambli Gitesh
Published: December 2023

With the rising surge of online toxicity, automating the identification of abusive language becomes crucial for improving online discourse. This study proposes a deep learning system that efficiently uses multiple labels to classify harmful comments using bi-directional Long Short-Term Memory (LSTM) networks. By leveraging contextual information, the bi-LSTM model achieves state-of-the-art performance in classifying subtle forms of toxicity such as threats, insults, identity hate, and obscenity. The model achieves above 95\% accuracy on benchmark datasets with rigorous data processing, optimized neural architecture, and the utilization of FastText embeddings to handle words that are not in the vocabulary. This technique can automatically filter different levels of toxicity, promoting positive online interactions when integrated into online platforms. The proposed study outlines an end-to-end pipeline incorporating recent NLP advancements and deep contextualized language models to address contemporary challenges in AI-enabled content moderation.
A Streamlined Approach towards Monkeypox Detection
Authors: Kulkarni Sarvesh , Oza Jay , Patil Abhijit , More Rishi , Kambli Gitesh , Maity Amit
Published: December 2023

Monkeypox has recently emerged as a public health emergency with rising cases worldwide. Early clinical diagnosis is challenging due to symptom overlap with other diseases, but characteristic skin lesions provide distinguishing visual cues. This work proposes a deep convolutional neural network (CNN) tailored for automated monkeypox screening from lesion images. A dataset of over 3000 dermatological images was compiled, with data augmentation to enhance diversity. The CNN architecture comprised convolutional blocks for feature extraction and dense layers for classification. Rigorous training and cross-validation were conducted over 100 epochs to optimize model performance. On an unseen test set, the model achieved 86.87\% accuracy in classifying monkeypox lesions, with 94\% precision, 79\% recall and 86\% F1-score. These metrics were better than baseline models, indicating reliable screening potential. Though the model overlooked some atypical presentations, successes showcase utility for mass case-finding. As monkeypox monitoring intensifies, robust computer vision approaches can assist clinicians through explainable, real-time forecasts. Prospective validation across demographics and integration with clinical workflows is warranted before full-scale deployment. Overall, the study demonstrates deep learning's promise in tackling the monkeypox outbreak through enhanced diagnosis.
Big Ethernet Switches Help to Historical HPC Topology Challenge
Authors: Vasilenko Eduard , Wang Haibo , Dai Longfei
Published: December 2023

HPC (high-performance computing) plays a more important role now because of AI training needs for big models. The latest HPC requirement is to scale up to fifty thousand processors (or servers). HPC is based on the direct memory exchange between processors that is typically called “interconnect”. Historically, it was a big scientific challenge to define a topology for interconnect due to the low scalability of switching elements. Initially, on-board switching ASICs supported below 10 directions/buses which creates a challenge to scale even to hundreds of processors. Different topologies invented at that time (like Torus) have many hops for the traffic that greatly increase cost and latency but decrease reliability. Moreover, many compromises resulted in the requirement for applications to be engineered in such a way as to distribute traffic unequally inside and between local groups. Later, Infiniband external switches greatly improved the scalability to dozens of interfaces permitting to creation of much more scalable topologies (like Dragonfly) with less number of hops. Infiniband considerably improved latency and cost. Yet, the scale of Inifiband still pushes for restrictions (like performance) that are far from optimal. The latest generation of Ethernet switches achieved the scale (hundreds of interfaces) that is enough to create topologies with minimal compromise or even without any compromise at all: the minimal number of hops that results in minimal cost and latency, maximum reliability, wire-speed or close to it (regulated at design time), no need for network load distribution at the application layer. The scale without any compromise could satisfy the biggest AI training requirements. The scale with minimal compromises could be 8 times high then needed now.
A Direct Modularization Method for Multi-cell to Multi-cell Equalizer with Large Cell Count
Authors: Dam Shimul , John Vinod
Published: December 2023

A modular voltage equalizer can be used in a wide variety of applications without changing the module design. However, the modularization of an equalizer topology often involves slow inter-module equalization, additional energy loss, and cost. A study on available modularization techniques shows that the transformer-based equalizers offer simple and low-cost modularization methods. Conversely, the transformer-less equalizers can offer more compact design, but their modularization methods suffer from either high complexity and loss, or slow inter-module equalization speed. In this work, one two-winding transformer is introduced into a transformer-less equalizer module to leverage the modularization benefits of transformer isolation while still achieving a more compact design than transformer-based equalizers. The proposed direct modularization method achieves fast inter-module equalization and fixed design for a variety of applications with a small increase in cost and size. The proposed method is implemented on a transformer-less equalizer and its effectiveness is validated experimentally with a three-module prototype.
Topology Identification and Parameters Estimation of LV Distribution Networks using Open GIS Data
Authors: Liu Dong , Giraldo Juan S. , Palensky Peter , Vergara Pedro P.
Published: December 2023

The topology of low-voltage distribution networks (LVDNs) is crucial for system analysis, e.g., distributed energy resources (DERs) integration, network hosting capacity analysis, state estimation, and electric vehicle charging management. However, it is frequently unavailable or incomplete. We develop a data-driven topology identification approach for DNs with a high proportion of underground cables. The proposed approach exploits the fact that underground cables usually follow the street pattern, thus relying on open street map (OSM) and smart meter (SM) data. Three stages compose the proposed approach: In the first stage, a hierarchical minimum spanning tree algorithm is proposed to generate the initial topology with an accurate number of sub-branches from the pre-processed OSM data and peak demand. In the second stage, based on the limited SM data, the location of breakpoints in mesh topology caused by circle roads is verified and reconstructed to guarantee the radial structure of LVDNs. Finally, given multiple incomplete SM datasets, three data-driven optimization models based on a state estimation model are constructed to mitigate the error of cable length induced by OSM data. The feasibility of the proposed topology identification approach is verified on three actual LVDNs in the Netherlands and multiple incomplete SM datasets.
Love Wave acoustic sensors behavior in complex liquids. Multiparameter sensing using acoustic and electrical signals
Authors: Choudhari Asawari
Published: December 2023

Love wave (LW) acoustic sensors are promising devices for biochemical detection in the liquid media. However, their application for in-situ biochemical detection especially in the turbid liquid medium is not yet explored. Turbid liquids are complex in nature with both mechanical and electrical characteristics. These characteristics could be reliably estimated with LW acoustic sensor response, where some of the dedicated sensors based on electrochemistry or optical principle show limitations. This paper presents experimental responses of LW acoustic sensor to turbid liquids based on Formazin solutions and its comparison to the spectrophotometer response. Analysis of sensor’s electro-acoustic response is used to better characterize the sensor performance with turbid liquids. Further, this paper demonstrates the feasibility of LW acoustic sensor as a multiparameter sensing unit by estimating the influence of electro-mechanical parameters of the turbid liquids on the overall response of the sensor.
Measuring the Relationship Between Self-Efficacy Beliefs and Performance Attainments of First Year Engineering Students in the Programming Course
Authors: El Khoury Joelle , Safa Nehme. , Khoury Jawad , Nasrallah Rita
Published: December 2023

This article targets first-year engineering students’ self-efficacy beliefs and their relationship with performance attainments in the first half of the semester and the final grade of the programming course. Self-efficacy is the belief in one’s capabilities to reach a desired goal or outcome by setting and implementing the required courses of action. In education, self-efficacy is crucial to academic growth, for it helps students take charge of their own learning, develop their skills, set goals, and regulate their motivation in order to accomplish these goals. Considering the difficulty of the programming course, self-efficacy plays a vital role in the challenges the students face in the programming course, their magnitude, and the skills the students use to overcome them. Self-efficacy beliefs were measured through a survey administered twice to 118 engineering students in the first half of the semester and correlated with the first quiz of the semester and the midterm grade, then with the final grade. The analyses targeted all engineering students, then each engineering major separately: Computer, Electrical, Mechatronics, Mechanical, and Industrial engineering. The results showed that there is in fact a relationship between students’ self-efficacy beliefs and performance in the programming course. However, the relationship was shown to be inverse in the first half of the semester but positive with the final grade. These findings highlight the importance of student efficacy beliefs especially in the first half of the semester while also presenting new empirical findings regarding each engineering major targeted.
Sliding Mode Controller for Robust Consensus Control of Multirobot System
Authors: Sarkar Aditya
Published: December 2023

This letter proposes and analyzes three different methods to achieve robust consensus control in a leader–follower multiagent system framework prone to bounded disturbance in finite time. In the first method, a sliding mode surface is proposed using the basic definition of consensus. An upper bound on noise is considered while designing the surface. Then a Lyapunov stability analysis is used to determine the control law. Second method deals with using a high-gain disturbance observer to dynamically estimate the noise. A sliding mode surface was then developed using the estimates. Further, a Lyapunov analysis was done to show the stability of the system. Lastly, a new sliding surface based on High Gain DIsturbance Observer is proposed which alleviates the problem of mismatched uncertainties. A Lyapunov analysis was then performed to ensure convergence of the system in finite time. The robustness of the proposed approach is validated through simulations.
Conduction and Electric Fields in Dielectric Liquids in Needle-to-Plane Gaps
Authors: hestad øystein , Aakre Torstein , lundgaard lars
Published: December 2023

Paper explaining measured conduction currents iin a needle to plane gap in a mineral oil and an ester liquid when applying a high voltage. There are large differences between mineral oil and ester.
Novel KLD-based Resource Allocation for Integrated Sensing and Communication
Authors: Kloob Yousef , Al-Jarrah Mohammad Ahmad , Alsusa Emad , Masouros Christos
Published: December 2023

In this paper, we introduce a novel resource allocation approach for integrated sensing-communication (ISAC) using the Kullback–Leibler divergence (KLD) metric. Specifically, we consider a base-station with limited power and antenna resources serving a number of communication users and detecting multiple targets simultaneously. First, we analyze the KLD for two possible antenna deployments, which are the separated and shared deployments, then use the results to optimize the resources of the base-station through minimising the average KLD for the network while satisfying a minimum predefined KLD requirement for each user equipment (UE) and target. To this end, the optimisation is formulated and presented as a mixed integer nonlinear programming (MINLP) problem and then solved using two approaches. In the first approach, we employ a genetic algorithm, which offers remarkable performance but demands substantial computational resources; and in the second approach, we propose a rounding-based interior-point method (RIPM) that provides a more computationally-efficient alternative solution at a negligible performance loss. The results demonstrate that the KLD metric can be an effective means for optimising ISAC networks, and that both optimisation solutions presented offer superior performance compared to uniform power and antenna allocation. 
Bus Tracking and Arrival Prediction System
Authors: Karunathilake Sachini
Published: December 2023

 —This project is a GPS (Global Positioning System) based system which helps passengers to know the expected time of arrival of the bus to their prospective halt or a particular location using present GPS data of the passenger and the bus. Within the ‘Bus Tracking and Arrival Prediction System’, each bus has a GPS tracker to track the bus. GPS tracker on the bus is used to locate the bus its coordinates and the speed of the bus are pushed on to the server to calculate an accurate arrival time to a user desired destination. Moreover, the server uses the received information on buses location and speed to identify the moving patterns. So, the owners of the private buses or the relevant authorities can monitor the moving pattern of their fleet and will be able to give feedback to the users. All system information is maintained in a database. Stored information will help to develop better algorithms in the future on predicting more accurate arrival time of their next bus service to the passengers 
Channel Coding Method Based on Weighted Probability Model
Authors: Wang Jielin
Published: December 2023

 X is a discrete memoryless binary source , Sequence X undergoes lossless transformation to satisfy the requirement “Each 1 is separated by one or more zeros", "Each 0 is separated by one or two 1s" or similar condition , These conditions are the decision conditions for error detection in channel transmission. A new channel coding method is proposed based on a weighted probability model for lossless coding , The encoding rate and encoding and decoding steps of different conversion methods are different , It is proved that the decoding error probability can reach 0 when the code length is long enough. In the simulation experiment of BPSK signal in AWGN channel, At 0.5 bit rate, the proposed method improves 1.1dB over Polar code when FER is 0.001, and 1.4dB over Polar code when FER is 0.0001. 
The graphene application for the Reconfigurable Intelligent Surface (RIS) design for the NOMA-RIS-MIMO system. Phenomenological view.
Authors: Kontorovich Valeri
Published: December 2023

The material is dedicated to various aspects of graphene metasurface implementation of the Reconfigurable Intelligent Surface (RIS) in the framework of the incoherent paradigm for NOMA-RIS-MIMO transmission design. Digital coded model of the metasurface is considered in the framework of the general representation of the graphene metasurface as a planar antenna array (PA). The simplification of the PA in the form of the non-uniform ULA (n-ULA) array and uniform linear array (ULA) is explained as well. In this regard array orthogonalization was applied in the form of the universal Prolate Spheroidal Wave Functions (PSWF) eigenbasis and it was used for the formulation of the RIS controller algorithm.
Dynamic Conjugate Gradient Unfolding for Symbol Detection in Time-Varying Massive MIMO
Authors: Olutayo Toluwaleke , Champagne Benoit
Published: December 2023

This work addresses symbol detection in timevarying Massive Multiple-Input Multiple-Output (M-MIMO) systems. While conventional symbol detection techniques often exhibit subpar performance or impose significant computational burdens in such systems, learning-based methods have shown potential in stationary scenarios but often struggle to adapt to nonstationary conditions. To address these challenges, we introduce a hierarchy of extensions to the Learned Conjugate Gradient Network (LcgNet) M-MIMO detector. Firstly, we present Preconditioned LcgNet (PrLcgNet), which incorporates a preconditioner during training to enhance the uplink M-MIMO detector’s filter matrix. This enhancement enables the detector to achieve faster convergence with fewer layers compared to the original, nonpreconditioned approach. Secondly, we introduce an extension of PrLcgNet, known as the Dynamic Conjugate Gradient Network (DyCoGNet), specifically designed for time-varying environments. DyCoGNet leverages self-supervised learning with forward error correction, enabling autonomous adaptation without the need for explicit labeled data during training. It also employs metalearning, facilitating rapid adaptation to unforeseen channel conditions. Our simulation results demonstrate that PrLcgNet achieves faster convergence, lower residual error, and comparable symbol error rate (SER) performance to LcgNet in stationary scenarios. Furthermore, in the time-varying context, DyCoGNet exhibits swift and efficient adaptation, achieving significant SER performance gains compared to baseline cases without metalearning and online self-supervised learning.
Video Conferencing Technologies: Past, Present and Future
Authors: Joskowicz Jose
Published: December 2023

This paper describes the historical trajectory of video conferencing systems, spanning from their earlier mechanical and analog origins in the 1920s to the sophisticated IP-based services delivered from the cloud in the 2020s. Each technological age is examined, highlighting the technical and functional aspects that characterized its evolution. Commercial landmarks of each age are presented providing a comprehensive overview of the most prominent offerings at pivotal moments in the timeline. By examining the past and speculating on the future, this paper aims to provide a holistic understanding of the development, current state, and forthcoming trends in video conferencing technology.
Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit
Authors: Meng Fanfei , Zhang Lele , Chen Yu , Wang Yuxin
Published: December 2023

Transformer requires a fixed number of layers and heads which makes them inflexible to the complexity of individual samples and expensive in training and inference. To address this, we propose a sample-based Dynamic Hierarchical Transformer (DHT) model whose layers and heads can be dynamically configured with single data samples via solving contextual bandit problems. To determine the number of layers and heads, we use the Uniform Confidence Bound algorithm while we deploy combinatorial Thompson Sampling in order to select specific head combinations given their number. Different from previous work that focuses on compressing trained networks for inference only, DHT is not only advantageous for adaptively optimizing the underlying network architecture during training but also has a flexible network for efficient inference. To the best of our knowledge, this is the first comprehensive data-driven dynamic transformer without any additional auxiliary neural networks that implement the dynamic system. According to the experiment results, we achieve up to 74\% computational savings for both training and inference with a minimal loss of accuracy.
A Physics-Constrained GAN for Incidence Angle Dependence Estimation of Arctic Sea Ice C-Band Backscatter
Authors: Kortum Karl , Singha Suman , Spreen Gunnar
Published: December 2023

The incidence angle dependence of synthetic aperture radar (SAR) backscatter from sea ice has been observed to differ between ice types. This is indicative of its complex dielectric and backscattering properties and cannot be analytically determined or easily modelled. Without assumptions this dependence can only be measured from SAR through multiple observations of the same ice. Due to the sea ice drift, doing so at high resolution is time inefficient and to some extent only feasible for land fast ice. Thus, the incidence angle dependence can currently not be easily related to ground measurements or be fully exploited for classification tasks. By reformulating the problem of incidence angle dependence to a domain transfer task and making use of known physical relations, we can train a Generative Adversarial Network (GAN) to predict the incidence angle dependence from a single Sentinel-1 satellite scene patch-wise using no only local data. The network's predictions are validated for multiyear and first-year ice backscatter using coincident ICESat-2 altimeter measurements. The network suggests a diverse incidence angle dependence in the HV channel, that so far have been observed only scarsely. The results of this study are a novelty in sea ice remote sensing insofar that artificial intelligence manages to solve a task that would be nearly impossible for a human observer.
Strategic Alchemy: The Role of AI in Transforming Business Decision-Making
Authors: Fomuso Ekellem Elizabeth Ango
Published: December 2023

"Strategic Alchemy: The Role of AI in Transforming Business Decision-Making" is a comprehensive research paper that delves into the revolutionary impact of Artificial Intelligence (AI) on strategic management and business decision-making processes. It covers a broad range of topics including the evolution of AI, its integration into business strategies, sector-specific impacts, real-world applications, and the inherent challenges and ethical considerations of incorporating AI into business. The paper emphasizes AI's significant role in transforming industries and decision-making paradigms, advocating for ethical transparency, continuous learning, and collaboration in the adoption of AI-driven strategies. It provides a detailed and enlightening overview of AI's pervasive influence and transformative potential in contemporary business landscapes. 
Optimisation-based Reference Generation for Off-tracking Minimisation of Generalised N-trailers Vehicles
Authors: Deniz Nestor Nahuel , Jorquera Franco , Cheein Fernando Auat
Published: December 2023

In this study, we introduce an innovative off-tracking optimisation-based framework designed for Generalised N-Trailer vehicles. The task of generating feasible references for the segments of the interconnected vehicle has historically been burdensome. This challenge becomes even more pronounced as the number of trailers increases, particularly when the quantity of trailers fluctuates during operations.

Our proposed method address this complexity by autonomously generating pose references for both the tractor and each trailer of the vehicle. This is achieved through a parameterisation of the desired path and attitudes to be traversed. Notably, our approach incorporates the system's model into the optimisation process as a constraint. This guarantees that the generated reference remains within the bounds of feasibility, even when the nominal path does not.

Several simulated and field experiments with a tractor pulling 2 trailers show the effectiveness of the proposed method.
Adaptive Reinforcement Learning with LLM-augmented Reward Functions
Authors: Place Alex
Published: December 2023

Learning in sequential decision making problems can be significantly affected by the choice of reward function. Although crucial for learning success, reward function design remains a complex task, requiring expertise and not always aligned with human preferences. Large Language Models (LLMs) offer a promising avenue for reward design through textual prompts that leverage the prior knowledge and contextual reasoning of LLMs. Despite their potential, LLM responses lack guarantees and their reasoning abilities are poorly understood. To mitigate potential LLM errors, we introduce an alternative approach: learning a new reward function utilizing LLM outputs as auxiliary rewards. This problem is tackled through a bi-level optimization framework, showcasing the method's proficiency in both optimal reward acquisition and adaptive reward shaping. The proposed approach demonstrates robustness and effectiveness, offering a novel strategy for enhancing reinforcement learning outcomes.      
Efektivitas Aplikasi Pendaftaran Rawat Jalan RSUD XYZ
Authors: Susetyo Aji Nugroho Haries Anom , . Sonhaji
Published: December 2023

Perkembangan era digital telah mengubah paradigma interaksi masyarakat dalam proses bisnis dengan pemanfaatan teknologi informasi. Salah satu dampak signifikan adalah penerapan digitalisasi dalam RSUD XYZ, termasuk aplikasi pendaftaran rawat jalan berbasis mobile. Aplikasi ini berhasil mengatasi permasalahan antrian pendaftaran, meningkatkan efisiensi administrasi, dan mempercepat penanganan pasien oleh petugas medis. Penelitian ini berfokus pada efektivitas aplikasi pendaftaran rawat jalan RSUD XYZ, menggunakan Completion Rate sebagai landasan pengukuran usability yaitu pada sisi efektivitas. Completion Rate digunakan untuk mengukur sejauh mana pengguna berhasil menyelesaikan tugas yang diberikan oleh aplikasi. Penelitian ini bertujuan untuk mengukur sejauh mana aplikasi pendaftaran rawat jalan memenuhi harapan pengguna dalam hal efektivitas. Dengan menganalisis Completion Rate, penelitian ini memberikan wawasan berharga bagi pengembang aplikasi untuk memahami kualitas aplikasi yang mereka ciptakan. Diharapkan penelitian ini memberikan kontribusi yang bermakna dalam mengoptimalkan pengalaman pengguna dan efisiensi layanan di RSUD XYZ.
Urethral Prolapse in a Post-Menopausal Woman at the Sominé Dolo Hospital in Mopti (Mali)
Authors: Kone Mory , Cisse Dramane , Traore Djibril , Fofana Youssouf , Théra Thioukany David , Diaby Souleymane
Published: December 2023

Urethral mucosal prolapse is a rare and benign condition, which consists of a circular eversion of the distal urethral mucosa through the urinary meatus. It occurs most often in prepubertal and postmenopausal women for whom lack of estrogen and poor pelvic support remain the predisposing factors. Its presence in the post-menopausal period is rare. We report a case of urethral prolapse in a postmenopausal woman at the Sominé Dolo hospital in Mopti who presented with persistent urinary symptoms. We present a case of urethral prolapse observed in a 66-year-old woman referred to the urology consultation and who presented recurrent urinary symptoms with multiple treatments based on antibiotics and medicinal plants without success for a year. Physical examination noted a circular, non-reducible and non-painful swelling at the level of the urethral meatus with bleeding on contact, consistent with urethral prolapse. The option of surgical excision was discussed with the patient. The patient underwent surgery and the postoperative course was simple.
Extracorporeal Shock Wave Lithotripsy: Experience of the Urology Department at the Oued Eddahab Agadir Military Hospital
Authors: Abdessamad El Bahri , Omar Jendouzi , Aziz Lamghari , Nabil Louardi , Mohammed Alami , Ahmed Ameur
Published: December 2023

Introduction: Since its invention, extracorporeal lithotripsy has been the reference treatment for all stone locations and sizes. Materials and methods: In this retrospective study, we present our experience of 116 patients treated for urinary calculi by extracorporeal lithotripsy (DORNIER Sigma 3 MedTech) performed in the urology department of the oued eddahab Agadir military hospital between June 2020 and July 2022. Results: The average age of our patients was 51, with extremes ranging from 23 to 79, with males the most frequently affected. The number of sessions per patient varied from one to four, with an average of 2.5 sessions. There is no particular anatomical site for renal lithiasis. A single stone is enough to reveal the symptomatology of urinary lithiasis. Conclusion: Despite the relative simplicity of the technique and its low morbidity, the indication must be carefully considered, taking into account factors predictive of success and failure.
Ocular Metastasis from Breast Carcinoma: A Case Report
Authors: Sassi S. , Hassouni F. , Benbella L. , Mohtarim R. El , Rouas L. , Lamalmi N.
Published: December 2023

Background: Eye metastases are a rare event in cancer, patients with breast cancer being the most common primary site (28.5%–58.8%). Patients with ocular metastatic disease can present with a variable clinical picture. Most often presents as a cutaneous or subcutaneous nodule simulating a chalazion but with rapid growth and possible superficial ulceration. The definitive diagnosis is based on the anatomopathological analysis of the lesion. The treatment remains palliative and the prognosis remains poor. Objectives: To further define the histopathologic features of breast carcinoma conducive to orbital metastasis. Methods: We report a case of ocular metastasis in a 46-year-old woman presenting with right eye pain. She had been treated for ductal carcinoma breast cancer with lymph node and bone metastases 5 years ago; a mastectomy with lymph node dissection was performed. The patient consults us for unilateral eyelid swellings evolving for 4 months. On examination, we find well-limited rounded tissue formation in the right upper eyelid ulcerated in places. Oculomotricity was preserved and the rest of the examination was unremarkable. A biopsy of the lesion was carried. Results & Conclusions: Microscopic examination revealed a fibrofatty tissue seat of a tumoral proliferation made of spans and cords, of carcinomatous cells with moderate atypia showing some mitosis figures. The stroma is fibrous. Immunohistochemical studies showed positive immunoreactivity to pancytokeratin, estrogen receptor, progesterone receptor and HER-2, the results were in favor of a metastatic breast carcinoma. In summary, In the case of a metastatic orbital tumor, breast cancer should be considered as a possible source, especially in elderly women. Integrating the clinical information with histopathological findings is warranted.
Dari Barter ke Online Shop: Konvergensi Penggunaan Media di Masa Pandemi COVID-19
Authors: Utari Prahastiwi , Alkhajar Eka Nada Shofa , Widiyanti Emi , Hermawati Tanti
Published: December 2023

The COVID-19 pandemic has brought difficulties in various sectors of life, one of which is in the economic field, especially regarding the existence of traditional market traders. The lack of visitors due to the large-scale restrictions on people’s movement and the fear of contracting COVID-19 when interacting with other traders or buyers make the lives of traditional market traders more complicated and slumped. Indo Barter Community is a community that conducts a bartering process with traditional market traders to help them meet their daily needs. The bartering process is not only done conventionally using direct communication but also using digital media. Recording the entry and exit of goods, determining the selling price, and managing the payment system are done through a computerized program. In addition, the Indo Barter community also re-markets the barter results through WhatsApp and Instagram. The increasing customer demand has led the Indo Barter community to expand the market into an online shop.
Pengembangan Model Latihan Kebugaran Jasmani Berbasis Permainan Tradisional Sebagai Aktivitas Belajar Mahasiswa Dalam Mata Kuliah Belajar Gerak
Authors: Mawardinur Mawardinur , Aristiyanto Aristiyanto , Daulay Dicky Edwar , Keliat Pedomanta , Amalia Nazwa
Published: December 2023

Inovasi pembelajaran belajar gerak yang efektif dan menyenangkan perlu dilakukan secara berkala  agar menambah alternatif model pembelajaran yang dapat memacu peningkatan motivasi ibelajar mahasiswa. Penelitian ini imerupakan jenis penelitian dan pengembangan yang bertujuan untuk: (1) analisis, (2) perencanaan, (3) ipengembangan, (4) iimplementasi dan (5) evaluasi. Tempat dan subyek penelitian yaitu mahasiwa STOK Bina Guna berjumlah 30 orang. Data penelitian dikumpulkan menggunakan angket skala nilai. Hasil penelitian adalah (1) model kebugaran jasmani berbasis permainan tradisional sebagai aktivitas belajar mahasiswa yang diberi nama model latihan three games. (2) model latihan kebugaran jasmani berbasis permainan tradisional three games memenuhi kategori “sesuai” (layak) digunakan sebagai bentuk aktivitas belajar mahasiswa pada mata kuliah belajar gerak
Hubungan Antara Support Family Dengan Work Life Balance Pada Ibu Yang Bekerja Di PT X
Authors: Ramdhania Tesya , Yuniasanti Reny
Published: December 2023

Penelitian ini bertujuan untuk mengetahui hubungan antara Support Family dengan Work Life Balance terhadap ibu yang bekerja. Kriteria subjek dalam penelitian ini adalah ibu yang bekerja di perusahaan X, sudah memiliki anak minimal 1, masa kerja minimal 3 bulan. Adapun jumlah subjek penelitian ini sebanyak 100 orang. Penelitian metode analisis data yang digunakan untuk menguji hipotesis analisis korelasi product moment pada penelitian ini adalah teknik korelasi (person correlation) yang di kembangkan oleh Karl Pearson. Pengambilan data menggunakan skala Support Family dan skala Work Life Balance. hipotesis pada penelitian ini adalah terdapat hubungan positif antara Support Family dengan Work Life Balance pada ibu yang bekerja. Semakin tinggi  Support Family , maka semakin tinngi juga Work Life Balance  pada ibu yang bekerja. Begitu pun sebaliknya, jika Support Family rendah maka semakin rendah pula Work Life Balance pada ibu yang bekerja. Dengan hasil koefisien (rxy) sama dengan 0.261  dan tingkat signifikansi (p) sama dengan 0.000. (p lebih kecil dari 0.050). Support family menyumbang 45,7 persen dari total pengaruh, sementara faktor lain yang tidak diidentifikasi oleh para peneliti menyumbang 54.3 persen.