bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Constructions of the Prefer-Same and Prefer-Opposite de Bruijn Sequences
Authors: Sala Evan , Sawada Joe , Alhakim Abbas
Published: July 2024

The greedy Prefer-same de Bruijn sequence construction was first presented by Eldert, Gray, Gurk, and Rubinoff in 1958. As a greedy algorithm, it has one major downside: it requires an exponential amount of space to store the length

\(2^{n}\)

de Bruijn sequence. Though de Bruijn sequences have been heavily studied over the last 60 years, finding an efficient construction for the Prefer-same de Bruijn sequence has remained a tantalizing open problem. In this article, we unveil the underlying structure of the Prefer-same de Bruijn sequence and solve the open problem by presenting an efficient algorithm to construct it using

\(O(n)\)

time per bit and only

\(O(n)\)

space. Following a similar approach, we also present an efficient algorithm to construct the Prefer-opposite de Bruijn sequence.
Emergency Documentation and Virtual Model of a Rock Art Palimpsest from Garowe, Nugaal Region, Somalia
Authors: Frinchillucci Gianluca , Bortolini Eugenio , Biagetti Stefano , Urcia Alberto , Brucato Alessia , Geyre Sonkor , Warsame Ali Abdullah , Prizzi Federico , Spigonardi Francesco , Conci Thomas , Natali Luca , Madella Marco
Published: July 2024

Puntland is a critically relevant and yet virtually unexplored region for the past of Eastern Africa. Here we present a newly documented cave with multi-layered rock art found surveying Nugaal region, Somalia, in the framework of an international project aimed at cultural heritage preservation and cooperation.
DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors
Authors: Li Chia-Hao , Jha Niraj K.
Published: July 2024

Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge devices have enabled ML-driven disease detection for smart healthcare. Conventional ML-driven methods for disease detection rely on customizing individual models for each disease and its corresponding WMS data. However, such methods lack adaptability to distribution shifts and new task classification classes. In addition, they need to be rearchitected and retrained from scratch for each new disease. Moreover, installing multiple ML models in an edge device consumes excessive memory, drains the battery faster, and complicates the detection process. To address these challenges, we propose DOCTOR, a multi-disease detection continual learning (CL) framework based on WMSs. It employs a multi-headed deep neural network (DNN) and a replay-style CL algorithm. The CL algorithm enables the framework to continually learn new missions in which different data distributions, classification classes, and disease detection tasks are introduced sequentially. It counteracts catastrophic forgetting with either a data preservation (DP) method or a synthetic data generation (SDG) module. The DP method preserves the most informative subset of real training data from previous missions for exemplar replay. The SDG module models the probability distribution of the real training data and generates synthetic data for generative replay while retaining data privacy. The multi-headed DNN enables DOCTOR to detect multiple diseases simultaneously based on user WMS data. We demonstrate DOCTOR’s efficacy in maintaining high disease classification accuracy with a single DNN model in various CL experiments. In complex scenarios, DOCTOR achieves 1.43× better average test accuracy, 1.25× better F1-score, and 0.41 higher backward transfer than the naïve fine-tuning framework, with a small model size of less than 350 KB.
A Method for Comparing Time Series by Untangling Time-Dependent and Independent Variations in Biological Processes
Authors: Thottupattu Alphin J. , Sivaswamy Jayanthi
Published: July 2024

Biological processes like growth, aging, and disease progression are generally studied with follow-up scans taken at different time points, i.e., image time series (TS) based analysis. Image TS represents the evolution of anatomy over time, but different anatomies may have different structural characteristics and temporal paths. Therefore, separating the time-dependent path difference and time-independent basic anatomy/shape changes is important when comparing two image TS to understand the causes of the observed differences better. A method to untangle and quantify the path and shape difference between the TS is presented in this paper. The proposed method is evaluated with simulated and adult and fetal neuro templates. Results show that the metric can separate and quantify the path and shape differences between TS.
Large Language Models for Conducting Advanced Text Analytics Information Systems Research
Authors: Ampel Benjamin , Yang Chi-Heng , Hu James , Chen Hsinchun
Published: July 2024

The exponential growth of digital content has generated massive textual datasets, necessitating the use of advanced analytical approaches. Large Language Models (LLMs) have emerged as tools that are capable of processing and extracting insights from massive unstructured textual datasets. However, how to leverage LLMs for text analytics Information Systems (IS) research is currently unclear. To assist the IS community in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework. Our proposed framework provides detailed recommendations grounded in IS and LLM literature on how to conduct meaningful text analytics IS research for design science, behavioral, and econometric streams. We conducted three business intelligence case studies using our TAISR framework to demonstrate its application in several IS research contexts. We also outline the potential challenges and limitations of adopting LLMs for IS. By offering a systematic approach and evidence of its utility, our TAISR framework contributes to future IS research streams looking to incorporate powerful LLMs for text analytics.
Thermoacoustic Instability in Combustors
Authors: Morgans Aimee S. , Yang Dong
Published: July 2024

Thermoacoustic instability is a flow instability that arises due to a two-way coupling between acoustic waves and unsteady heat release rate. It can cause damaging, large-amplitude oscillations in the combustors of gas turbines, aeroengines, rocket engines, etc., and the transition to decarbonized fuels is likely to introduce new thermoacoustic instability problems. With a focus on practical thermoacoustic instability problems, especially in gas turbine combustors, this review presents the common types of combustor and burner geometry used. It discusses the relevant flow physics underpinning their acoustic and unsteady flame behaviors, including how these differ across combustor and burner types. Computational tools for predicting thermoacoustic instability can be categorized into direct computational approaches, in which a single flow simulation resolves all of the most important length scales and timescales, and coupled/hybrid approaches, which couple separate computational treatments for the acoustic waves and flame, exploiting the large disparity in length scales associated with these. Examples of successful computational prediction of thermoacoustic instability in realistic combustors are given, along with outlooks for future research in this area.
Coalescence Dynamics
Authors: Eggers Jens , Sprittles James E. , Snoeijer Jacco H.
Published: July 2024

The merging of two fluid drops is one of the fundamental topological transitions occurring in free surface flow. Its description has many applications, for example, in the chemical industry (emulsions, sprays, etc.), in natural flows driving our climate, and for the sintering of materials. After the reconnection of two drops, strongly localized surface tension forces drive a singular flow, characterized by a connecting liquid bridge that grows according to scaling laws. We review theory, experiment, and simulation of the coalescence of two spherical drops for different parameters and in the presence of an outer fluid. We then generalize to other geometries, such as drops spreading on a substrate and in Hele–Shaw flow, and we discuss other types of mass transport, apart from viscous flow. Our focus is on times immediately after reconnection and on the limit of initially undeformed drops at rest relative to one another.
New Technologies for Monitoring Coastal Ecosystem Dynamics
Authors: Cavanaugh Kyle C. , Bell Tom W. , Aerni Karen E. , Byrnes Jarrett E.K. , McCammon Seth , Smith Madison M.
Published: July 2024

In recent years, our view of coastal ecosystems has expanded and come into greater focus. We are currently making more types of observations over larger areas and at higher frequencies than ever before. These advances are timely, as coastal ecosystems are facing increasing pressures from climate change and anthropogenic stressors. This article synthesizes recent literature on emerging technologies for coastal ecosystem monitoring, including satellite monitoring, aerial and underwater drones, in situ sensor networks, fiber optic systems, and community science observatories. We also describe how advances in artificial intelligence and deep learning underpin all these technologies by enabling insights to be drawn from increasingly large data volumes. Even with these recent advances, there are still major gaps in coastal ecosystem monitoring that must be addressed to manage coastal ecosystems during a period of accelerating global change.
Perspective–Opportunities for Industrial Applications of Pulse Electrolytic Processes Beyond Plating and Surface Finishing
Authors: Taylor E. Jennings
Published: July 2024

The author’s perspective is primarily derived from work with colleagues at Faraday Technology, Inc., a company he founded with the vision of developing pulse current/pulse reverse current (PC/PRC) based industrial plating innovations. DC plating processes generally require additives which must be monitored and replenished in order to ensure deposit properties. Due to the elimination of difficult to control/monitor additives, PC/PRC plating processes are more robust and easily controlled, less costly and/or safer and cleaner for workers and the environment. The initial vision evolved to include PC/PRC industrial surface finishing applications. PC/PRC enabled additive-free electrolytes for industrial plating and surface finishing applications are summarized. Emerging industrial applications of PC/PRC electrolysis beyond plating and surface finishing are presented.
Deciphering In Vitro and In Vivo Pharmacological Properties of Seed and Fruit Extracts of Flacourtia jangomas (Lour.) Raeusch
Authors: Ripa Farhana Alam , Alam Fowzia , Riya Fahmida Haque , Begum Yesmin , Eti Sharmin Akter , Nahar Nusratun , Ahmed Zebunnesa , Sharmin Sabrina
Published: July 2024

The objective of the study was to evaluate the pharmacological properties of the methanolic extract of Flacourtia jangomas (Lour.) Raeusch fruits (PFJM) and seeds (SFJM), along with their soluble fractions in ethyl acetate (fruit: PFJE; seed: SFJE) and chloroform (fruit: PFJC; seed: SFJC). Our phytochemical analysis of the examined extracts confirmed the presence of various therapeutically active phytoconstituents, including flavonoids, tannins, glycosides, and alkaloids. Employing the DPPH (2,2‐diphenyl‐1‐picrylhydrazyl) radical quenching method, SFJC exhibited the highest antioxidative potential, with an IC50 of 48.84, compared to ascorbic acid (IC50 21.77). The thrombolytic activity was assessed through rapid clot analysis of human blood samples, revealing that SFJC demonstrated the highest thrombolytic activity (60.99 ± 2.28%) compared to streptokinase (72.89 ± 2.19%). In the protein denaturation antiarthritic test, the PFJE and SFJC extracts exhibited significant potency, achieving results of 74.28 ± 1.16% and 79.25 ± 0.83%, respectively, at a dose of 500 μg/mL. All samples displayed notable anthelmintic activity by reducing Pheretima posthuma paralysis and death time in a dose‐dependent manner compared to albendazole. In both in vivo analgesic tests, SFJC demonstrated substantial (p < 0.01) pain inhibition percentages (tail immersion: 49.46%; acetic acid writhing: 66.43%) at a dose of 600 mg/kg. During neuropharmacological screening, all extracts significantly (p < 0.01;  p < 0.05) and dose‐dependently decreased the mice’s locomotion activity and motor balance. In the thiopental‐induced sedation assay, SFJC significantly decreased the sleep latency time (4.18 ± 0.24 min) and increased the duration of sleep time (85.20 ± 2.39 min) at a higher dose. All samples notably reduced blood glucose levels in the oral glucose tolerance test in a dose‐responsive manner, and SFJC exhibited a considerable hypoglycemic impact (7.38 ± 0.44 mmoles/L at 600 mg/kg). The frequency of diarrheal episodes in mice during the antidiarrhea assessment was significantly decreased by the tested plant samples. These findings can serve as a reference for future endeavors to isolate pure bioactive compounds from this plant for the development of novel phytomedicines.
Production Standardization and Nutritional Characterization of Rice Fritters Marketed in Cameroon
Authors: Saah Bruno Fotso , Foffe Hermann Arantes Kohole , Zokou Ronice , Dongmo Hygride , Naïm Saturnin , Ndjang Marie Madeleine Nanga , Simo Gustave , Klang Mathilde Julie
Published: July 2024

Rice fritter is a fermented food produced using traditional technology, and its consumption has become important these last years. However, as its technological production is not well mastered due to multitude production processes, information on the production process and the nutritional value of rice fritters remains unknown. This study was designed to produce rice fritters with good nutritional value by standardizing its production. A randomized cross‐study was carried out, and knowledge on the production process and the ingredients’ origin was collected from 298 consumers and 104 producers of 7 cities of Cameroon (Bafoussam, Banyo, Foumban, Foumbot, Yaoundé, Ngaoundéré, and Maroua). It was a randomize cross‐study. Thus, the nutritional characterization of rice fritters marked and the rheological properties of rice flours commonly used were carried out in the laboratory by using standardized methods. About 93.3% people producing rice fritter were women, from the north (40.4%) and permanent traders (60.6%). Moreover, 83.7% of producers used a mixture of rice–wheat flour while 16.3% used only rice flour. Producers used both local and imported rice grains. Marketed rice fritters contained ash (1.38%–2.17%), proteins (7.19%–10.35%), lipids (13.82%–21.72%), fibers (1.38%–2.54%), and carbohydrates (67.16%–74.17%). Independently of collection city and although the production steps were identical, some differences recorded in the step of making dough led to four commonly used production processes and fritters made by Process 2 (which differed from the others especially with the addition of milk (100–200 mL)) had the highest macro‐ and micronutrient contents.
Exploring the Inducement for Social Awareness Behavior and Optimal Control Strategy on Nipah Virus Transmission
Authors: Efat Saima , Kabir K. M. Ariful
Published: July 2024

Optimal control theory and evolutionary game theory are essential tools for comprehending and influencing the intricate behaviors of complex systems, particularly in the context of disease transmission and strategies for intervention. In this study, we leverage optimal control theory to address short‐term disease dynamics using a single season strategy. In contrast, evolutionary game theory guides our approach on a longer timescale through a repeated seasonal model. We employ a system of nonlinear ordinary differential equations to dissect how the dynamics of primary infections impact the spread of Nipah disease. Our novel dynamic system extends the classical susceptible‐infected‐recovered (SIR) model by introducing four distinct population categories: humans, bats, fruit, and animals. We delve into this epidemic model’s theoretical underpinnings, examining disease‐free and endemic equilibria to establish stability conditions. To address the challenge of optimally reducing the number of infectious individuals, we formulate an optimal control problem featuring four distinct control strategies. These strategies are deployed to mitigate disease transmission, all driven by a generalized incidence function. By identifying the optimal amalgamation of these strategies, we aim to minimize the infectious population. Decisions about the selection and execution of diverse disease control policies rest upon theoretical projections and numerical simulations conducted over a single season. Our study also incorporates evolutionary game dynamics, wherein individuals choose whether to adopt awareness and protection measures after the disease has circulated within the community. We meticulously explore the impact of such awareness and protection measures to underscore their significance within the context of the epidemic model across multiple time steps. Moreover, we systematically analyze the parameter properties within the epidemic model to address diverse real‐world scenarios.
Study on Strength and Micromorphology of Expansive Soil Improved by Cement–Coal Gangue under Dry–Wet Cycle
Authors: Yang Guijia , Mo Peng , Tian Quanquan , Miao Qinghai , Liu Dongtao , Pan Junkui
Published: July 2024

In order to improve the poor engineering characteristics of the expansive soil, such as cracking resulting from dry–wet swelling and shrinkage. In this study, coal gangue and cement were used as improving materials to improve the expansive soil. The research focuses on testing the efficacy of various coal gangue additions in enhancing the soil properties through compaction, liquid–plastic limit, expansion rate, unconfined strength, direct shear tests, as well as the dry–wet cycle stability of cement–coal gangue composite improved the samples. The findings suggest that as the coal gangue content raise, there is a corresponding decrease observed in both the maximum dry density and the expansion rate of the expansive soil. Simultaneously, the unconfined compressive strength (UCS) an increment from 0.07 MPa for the untreated soil to 0.6 MPa. Additionally, the inclusion of cement as a compound improvement agent substantially improves the UCS of the composite soil samples, with the highest recorded value reaching 3.14 MPa. The UCS of the composite improved soil is observed through three distinct stages: small increase, followed by a rapid decline, and ultimately reaching a state of gradual stabilization. Following 15 cycles of dry–wet cycles, the strength of the enhanced soil undergoes a reduction ranging from 26% to 57%. Scanning electron microscopy (SEM) analysis of the internal structure of the improved soil sample reveals that the cementitious material generated by cement hydration encapsulates the expansive soil particles and establishes connections with the coal gangue particles. However, fine cracks are observed in the cement hydration products near the surface of the coal gangue particles.
Supplementary Figure 1 from Epigenetic Induction of Cancer-Testis Antigens and Endogenous Retroviruses at Single-Cell Level Enhances Immune Recognition and Response in Glioma
Authors: Lai Thomas J. , Sun Lu , Li Kevin , Prins Terry J. , Treger Janet , Li Tie , Sun Matthew Z. , Nathanson David A. , Liau Linda M. , Lai Albert , Prins Robert M. , Everson Richard G.
Published: July 2024

Fig S1A-D
Figure 5 from Epigenetic Induction of Cancer-Testis Antigens and Endogenous Retroviruses at Single-Cell Level Enhances Immune Recognition and Response in Glioma
Authors: Lai Thomas J. , Sun Lu , Li Kevin , Prins Terry J. , Treger Janet , Li Tie , Sun Matthew Z. , Nathanson David A. , Liau Linda M. , Lai Albert , Prins Robert M. , Everson Richard G.
Published: July 2024

CTA induction but not downstream interferon-downstream stimulation by DAC is correlated with CpG demethylation in glioma cells. A, Heatmap and hierarchical clustering of differentially expressed CTA. Scale represents z-score of DESeq2 counts. B, Violin plot of beta value distribution between treatment conditions for CpG probes corresponding to differentially upregulated CTA (P-value = 0.028; Wilcoxon rank sum test with continuity correction). C, Scatter plot of all CpG island probes containing intersectional DNA methylation log2FoldChange and RNA expression log2FoldChange of annotated genes in black (n = 115,801 CpG island probes). CpG island probes corresponding to CTA with Pearson correlation are highlighted in (red; n = 535 CpG probes; n = 65 CTA genes). D, Enricher analysis of DESEQ2 differentially upregulated genes (Hallmark IFNγ P = 0.00548; Hallmark IFNα P = 0.00548; Hallmark inflammatory response P = 0.0426) and downregulated genes (Hallmark mTORC1 signaling P = 1.59e−3; Hallmark mitotic spindle P = 1.59e−3, Hallmark G2M Checkpoint P = 7.77e−5; Hallmark E2F targets P = 7.77e−5). Hallmark MSigDB signature adjusted P values determined using Fisher exact test adjusted by Benjamini–Hochberg correction. E, Violin plot of beta value distribution between treatment conditions for CpG probes corresponding to differentially upregulated DAC_UP_MSigDB_Hallmark genes (P-value = 0.497; Wilcoxon rank sum test with continuity correction). F, Scatter plot of intersectional DNA methylation log2FoldChange and RNA expression log2FoldChange CpG island probes. CpG probes corresponding to the Hallmark Interferon Alpha Response gene set with Pearson correlation are highlighted in blue (n = 471 probes; n = 53 genes).
Figure 1 from Epigenetic Induction of Cancer-Testis Antigens and Endogenous Retroviruses at Single-Cell Level Enhances Immune Recognition and Response in Glioma
Authors: Lai Thomas J. , Sun Lu , Li Kevin , Prins Terry J. , Treger Janet , Li Tie , Sun Matthew Z. , Nathanson David A. , Liau Linda M. , Lai Albert , Prins Robert M. , Everson Richard G.
Published: July 2024

CpG island methylation is associated with CTAG1A/B downregulation in the majority of gliomas and DAC time-dependently induces NY-ESO-1 expression via promoter demethylation for targeting by NY-ESO-1-specific TCR.A, Representative lollipop diagram of direct methylation PCR sequencing of immortalized GBM cell lines (U-251MG, BT142), immortalized meningioma cell line (CH-157MN), primary GBM cell lines (HK211, HK213, HK308, HK252), IDHwt (n = 17) and IDHmut (n = 10) patient samples. M, Methylated and U, Unmethylated determined from cytosine/thymine (C/T) peak ratio. B, Representative Western blot of native CH-157MN and U-251MG and U-251MG treated with 1 µmol/L DAC for 2 days. C, Cytotoxic analysis of U-251MG and CH-157MN cells over a 40-hour period when co-incubated with NY-ESO-1 TCR-transduced effector T cells (n = 2; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; unpaired t test). D, Representative Bis-Seq-TA cloning lollipop diagram and (E) percent methylated CpGs to total CpGs of first eight sites of analyzed CpG island in U-251MG cells treated with DMSO or 1-µmol/L DAC for 2 or 4 days. F, Average fold change of mRNA expression of treated U-251MG (n = 3; *, P < 0.05; one-way ANOVA). G, Cytotoxic analysis of treated U-251MG cells over a 40-hour period when co-incubated with NY-ESO-1 TCR-transduced effector T cells at E:T ratio of 1:1 and (H) at multiple E:T ratios (1:10, 1:5, 1:2, 1:1) at 20-hour (n = 3; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; multiple unpaired t tests).
Smart grid solutions for sustainable photovoltaic-electric vehicle integration in Bangladesh
Authors: Hossain Al Amin , Samad Abdus
Published: July 2024

Environmental concerns and the depletion of fossil fuel supplies are driving the rapid integration of photovoltaic (PV) systems into the electrical grid and electric vehicles (EVs) into the transportation sector. Issues like unpredictable power outages and shifts in demand require a cost-benefit analysis and efficient scheduling. In order to optimize PV power consumption and EV charging while taking seasonal variations into consideration, this study offers a novel solar-based grid-tied charging station with an improved scheduling technique. The existing charging station connected to the grid and solar promises not only reduced grid demand and cost savings, but also energy independence and environmental benefits. In the actual case in Rajshahi, Bangladesh, it is carried out using a Homer Grid case study. Bangladesh may promote environmental sustainability and resource conservation with this technology.
Power system stability improvement using fuzzy logic FACTS-UPQC conditioner
Authors: Lenjo Emmanuel , Kenfack Pierre , Nyobe Yome Jean Maurice
Published: July 2024

In power system, stability analysis becomes important to identify the level of stability and security of electrical power systems. This article proposes a flexible alternating current systems-unified power flow compensator (FACTS-UPQC) compensator installed in the high-voltage network to ensure stability of voltage and frequency in the power grid facing voltage dips, over-voltage and short-circuit faults. Thus, an artificial intelligence algorithm based on fuzzy logic method is implemented to have the appropriate values of FACTS-UPQC conditioner. The voltage stability improvement is demonstrated by the variation margin of amplitude and phase angle. Frequency stability aims to obtain a frequency within a minimal variation. A 14-bus test electrical system is modeled to implement the advanced control strategy. MATLAB/Simulink software is used to prove the functionality of the method in improving the stability of power system. The simulation results showed a reduction of harmonic distortion rate (HDR) and a minimization of the voltage variation range for the implemented fuzzy logic system compared with the literature.
System availability assessment and optimization of a series-parallel system using a genetic algorithm
Authors: Chaudhary Priya , Bansal Shikha
Published: July 2024

To optimize the operational availability of the series-parallel system and provide useful insights for maintenance planning, the study attempts to investigate the availability of a ball mill unit. These four different components make up the ball mill production system: “drum,” “ring-gear,” “gearbox,” and “electric motor.” There is a chain mechanism connecting all four components. The “ring gear” and “electric motor” components are composed of two independent units, one of which serves the desired purpose and the other is maintained in cold standby. The “drum” and “gearbox” of the components each contain only one unit. Therefore, a novel mathematical model is designed and implemented in this work by assuming arbitrary repair rates and exponentially distributed failure rates using the Markov process and Chapman-Kolmogorov equations. This study explored the availability with a normalization method and used genetic algorithm techniques to optimize ball mill availability. Putting this article into practice is of great benefit when developing an appropriate maintenance program. Through this, the study achieves maximum production. To investigate the behavior of several performance characteristics of the ball mill production system, numerical results and corresponding graphs are also specifically created for specific values of subsystem parameters, such as failure rate, and repair rate to increase the system’s overall efficiency.
Artificial neural network-based intelligent sensor-based electronic nose for food applications
Authors: Managuli Manjunath , Bagyalakshmi Kalimuthu , Shiny Malar Francis Rosy , Rubia Jebaraj Jency , Iderus Samat
Published: July 2024

Food commerce, especially for the general public, is greatly impacted by the capacity to identify and recognize chemical samples for food applications. Every chemical sample response has a unique, distinguishing smell. These advancements highlight the method of an artificial neural networks (ANN) to distinguish the distinctive fragrance from the reaction of substances. The categorization of various smell patterns has diminished confidence in ANN technology. Using an ANN technique and a sensor-based e-nose system for food applications, each chemical’s identification has been done commercially. The system comprises a 5-gas sensor selection that recognizes chemical talk while allowing for an improvement in permitting while falling gas is planned outside. To build a model of a different signal reaction, individual sensors are equally collected and merged into the innovation -favored sensor array. Demonstrates how it is related to the chemical test. The e-nose categorization has been tested with five different chemical samples and five different sensor classes. The e-nose approach, which comprises five sensors, can classify each chemical reaction model, starting with the results. With more sensors being employed, the classification accuracy of the precise chemical reaction improves. These data demonstrate that the ANN-based e-nose method promises a successful classification system for chemical sample responses for a characteristic odor sample.
Improving communication between can-sized satellite and ground control station for accuracy of data acquisition
Authors: Bin Zohari Mohd Hakimi , Yin Lam Hong , Bin Mokhtar Mohd Hezri
Published: July 2024

The can-sized satellite, ScoreSAT satellite is a small communications satellite. ScoreSAT helps to develop a platform for finding directions and the exact spot where a lack of communications signal occurs, as well as a real-time visual feed for analysis of communication during and after landing. The project focuses on the design of ScoreSAT and provides a real-time system for capturing real-time data during descent. The objective of the real-time system is to improve the accuracy and location of ScoreSAT data collection, which can provide pressure, humidity, temperature, altitude, latitude, and longitude readings. The main components of this platform are the hardware design that comprises the flight controller, GPS module, and telemetry kit, the software design, which are Mission Planner, and the real-time system (RTS). Based on the entire research, the compact design of the ScoreSAT and ground station was developed to provide alternative meteorological parameter monitoring to complement primary meteorology ground observation such as weather station and radiosonde and to enhance the reliability of the remote sensing observation for environmental studies concerning the factors determining the environment and atmospheric.
Optimizing channel capacity for B5G with deep learning approaches in MISO-NOMA-HBF and BFNN
Authors: Masud Muhammad Atique , Al Amin Ahmed , Islam Md. Shoriful , Mostafa Vaskor , Wahiduzzaman Md.
Published: July 2024

This study proposes the integration of a beamforming neural network (BFNN) and multiple-input single-output based non-orthogonal multiple access (MISO-NOMA) with hybrid beamforming (HBF) for cell edge users (CEU) in a millimeter wave (mmWave)-based beyond 5G cellular communication system. This system is referred to as MISO-NOMA-HBF-BFNN. The proposed scheme has been implemented to support multiple users simultaneously and also to considerably enhance and significantly improve the overall the sum channel capacity (SC) and user channel capacities. Additionally, the simulation results demonstrate the superiority of the proposed MISO-NOMA-HBF-BFNN scheme over the existing MISO-NOMA with HBF and MISO-OMA with HBFBFNN based schemes in terms of user capacities and SC.
A model proposal for enhancing cyber security in industrial IoT environments
Authors: Buja Atdhe , Apostolova Marika , Luma Artan
Published: July 2024

The revolution of the industrial sector in the automated one has happened with the use of the Industrial Internet of things (IIoT). They are providing unprecedented possibilities for connection, and automation. Also, the ubiquitous of IIoT has brought new cyber security challenges, putting sensitive data at risk. This research paper proposes a comprehensive model for enhancing the cyber security of IIoT systems. Our model integrates various countermeasures, including a proactive assessment of security vulnerabilities, examination of identified vulnerabilities, categorizing data, delivery of comprehensive reports, and assurance of effective countermeasures based on a cost-benefit approach, aligned with industry standards and frameworks. The proposed model aims to address the need for the development of robust and resilient cyber security solutions for IIoT environments. This research work introduces the proposed model's main functions, integration, workflow, and references. With this research, we contribute to the enhancement of cyber security in the IIoT environment by proposing a model that assists with proactive assessment, effective response, and informed decision-making. We envision that the proposed model will support industrial organizations in securing their IIoT systems against cyber threats, ultimately have stability and secure industrial operations.
Scientific landscape on opportunities and challenges of large language models and natural language processing
Authors: O. Roxas Rachel Edita , C. Recario Reginald Neil
Published: July 2024

This paper conducted a systematic review of Scopus-indexed publications on large language models (LLMs) and natural language processing (NLP) extracted in October 2023 to address the dearth of literature on their opportunities and challenges. Through bibliometric analysis, from the 1,600 relevant documents, the study explored research productivity, revealing both opportunities and challenges spanning research and real-world applications in education, medicine, and health care, citations, and keyword co-occurrence networks. Results highlighted distribution patterns and dominant players like Google LLC and Stanford University. Opportunities such as technological development in generative artificial intelligence (AI), were contrasted with challenges such as biases and ethical concerns. The intellectual structure analysis revealed prominent application areas in health and education and also emphasized issues such as AI divide and human-AI partnership. Improvement on the technology performance of LLM and NLP remains to be a challenge. Recommendations include further exploration of open research problems and bibliometric studies using other research databases given the research bias towards Scopus-indexed English publications.
A novel distributed generation integrated MFUPQC for active-power regulation with enhanced power quality features
Authors: Seshu Moturu , Sundaram Kalyana , Ramesh Maddukuri Venkata
Published: July 2024

The distributed generation (DG) scheme has become significant and advanced energy generation corridor for present power distribution system. This advanced DG scheme offers several merits such as flexible active power transfer, low transmission losses, maximize power efficiency, reduce transmission cost, expanding grid capacity, so on. It is motivated that, integration of such DG system in to multi-parallel feeder distribution system with enhanced power-quality features is considered as major problem statement. The proposed multi-functional unified power-quality conditioner (MFUPQC) device has robust design, reliable performance; specifically for addressing the voltage-current affecting PQ issues, regulation of active-power in multi-parallel distribution system. The fundamental goal of the MFUPQC device has been to operate as both a PQ improvement device and a DG integration device by implementing a new universal fundamental vector reference (UFVR) control algorithm. The suggested innovative control algorithm extracts the fundamental voltage and current reference signals with low computational response delay, simple mathematical formulations and without additional transformations which are also major problems identified in classical control schemes. This work focuses on design, operation and performance of MFUPQC device has been evaluated in both PQ and DG operations in a multi-parallel feeder distribution system through MATLAB/Simulink computing platform. The simulation results are illustrated with possible interpretation and analysis.
Electronic document management systems implementation across industries: systematic analysis
Authors: Anggraini Dian , Adi Kusworo , Suseno Jatmiko Endro
Published: July 2024

The construction sector’s pivotal role in the global economy faces challenges due to its dynamic nature. Inaccurate documentation impacts project cost management, underscoring the need for effective document management systems (DMS), including electronic document management systems (EDMS). This study conducts a systematic literature review to comprehensively examine EDMS implementation, utilization, and effectiveness across sectors. Analyzing peer-reviewed articles and scholarly sources reveals key themes, trends, and findings, providing insights into successful EDMS adoption and best practices. The review contributes evidence-based insights for practitioners, researchers, and policymakers, addressing gaps in knowledge and advancing understanding of EDMS in modern information management. Additionally, it presents a detailed breakdown of publication distribution across sectors, highlighting significant research areas like companies and businesses, education, and information technology and software. Furthermore, analysis of factors influencing employee behavior, including technical factors, employee’s personal characteristics, organizational factors, and trust, offers valuable insights into workplace dynamics. Overall, the study offers comprehensive insights into EDMS implementation, guiding future research, and organizational strategies.
A framework for reusable domain specific software component extraction based on demand
Authors: Basha N Md Jubair , Ganapathy Gopinath , Mohammed Moulana
Published: July 2024

The majority of organizations use an agile software development methodology. Standard analysis and design processes are abandoned due to the enormous demand of generating the product within time and budget. This may result in a lack of high-quality software while components are not constructively reused. The components are identified at a later stage in the majority of component approaches. To address such challenges, a methodology for extracting demand-based domain-specific software components from the repository was developed. The process for reusing current components is described in depth with various domain-specific components, and the suggested framework is for extracting demand-based reusable domain-specific software components.
Enhancing clinical decision-making with cloud-enabled integration of image-driven insights
Authors: Senkamalavalli Rajagopalan , Sankar Singaravel , Parivazhagan Alaguchamy , Raja Raju , Selvaraj Yoganand , Srinivas Porandla , Varadarajan Mageshkumar Naarayanasamy
Published: July 2024

Using the complementary strengths of Bayesian networks, decision trees, artificial neural networks (ANNs), and Markov models, this endeavor intends to completely revamp clinical decision-making. In order to provide instantaneous access to image-driven insights and clinical decision support systems (CDSS), want to create a revolutionary framework that merges these cutting-edge methods with cloud-enabled technologies. The proposed framework gives a comprehensive perspective of patient data by merging the probabilistic reasoning of Bayesian networks with the interpretability of decision trees, the pattern recognition abilities of ANNs, and the temporal interdependence of Markov models. This helps doctors to make more educated judgments based on a larger spectrum of information, leading to better patient outcomes. Healthcare workers can get to vital data from any place because to the cloud-enabled architecture's seamless scalability and accessibility. This not only increases the efficiency of decision-making, but also improves communication and cooperation between different medical professionals. This uses cutting-edge modeling strategies and cloud computing to pave a new path in clinical decision-making. This system has the potential to greatly enhance healthcare by integrating image-driven insights with CDSS, to the advantage of both patients and healthcare practitioners.
An improved surface solar radiation estimation model using integrated meteorological-air quality data
Authors: Boottarat Prakaykaew , Bin Salim Mohd Azli , Photong Chonlatee
Published: July 2024

This paper proposes an improved high-precision surface solar radiation estimation model using the integration of the local meteorological data and air quality index based linear regression analysis. The proposed model was evaluated and compared to 8 conventional models and one generated by the commonly used PVsyst simulation software. The actual solar radiation, meteorological data and air quality index collected over 10 years (during 2011-2021) from standard measuring stations located at the northern zone of Thailand were used for developing the models while the collected data year 2022 were used for validating the developed models compared to the conventional models. The statistical error estimations in terms of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used for the precision evaluation. The study found that the proposed models achieved better prediction results and the highest precision for monthly estimating of solar radiation than the other models by having the highest estimation precision of 94.70-97.19% compared to 87.53-96.74% of the conventional models and 90.38-95.96% of the PVsyst program.
The surprising influence of social commerce service quality on purchase intentions mediated by e-commerce
Authors: Suwarno Bukky , Dhewanto Wawan , Belgiawan Prawira Fajarindra
Published: July 2024

Social commerce has become a recent phenomenon and is poised to grow rapidly in the next few years. To better address customer behavior on social commerce platforms, it is imperative to acquire a comprehensive understanding of social commerce from the perspective of service quality. The objective of this research is to examine the dimensions of social commerce service quality and to reveal the factors influencing purchase intentions among the expanding user population. This study identified seven critical dimensions of social commerce service quality (website design, fulfilment, customer service, communication, contact, credibility, and security) that influence purchase intention. This research adopts a questionnaire survey method to collect data from social commerce users. Using PLS-SEM, the findings from an empirical analysis, conducted with a sample of 411 social commerce users, demonstrate that all measured dimensions significantly impact the intention to purchase. The findings also demonstrate that e-commerce has considerable influence on customer purchase intention as a partial mediator in social commerce. The findings hold significant implications for social commerce enterprises to increase customer attraction by identifying the motivations behind their purchasing decisions.
Multi-objective-trust aware improved grey wolf optimization technique for uncovering adversarial attacks in WSNs
Authors: Bannikuppe Srinivasiah Venkatesh Prasad , Ranganathasharma Roopashree Hejjaji , Ramanna Venkatesh
Published: July 2024

Wireless sensor network (WSN) is made of several sensor nodes (SN) that monitor various applications and collect environmental data. WSNs are essential for a wide range application, including healthcare, industrial automation, and environmental monitoring. However, these networks are susceptible to several security threats, underscoring the need for robust attack detection systems. Therefore, in this study, a multi-objective-trust aware improved grey wolf optimization (M-TAIGWO) is implemented to mitigate various attacks types. This implemented M-TAIGWO method is used to select secure cluster heads (CH) and routes to obtain secure communication through the network. The implemented M-TAIGWO provides improved security against malicious attacks by increasing the energy efficiency. The important aim of M-TAIGWO is to attain secured data transmission and maximize the WSN network lifetime. The M-TAIGWO method’s performance is evaluated through energy consumption and delay. The implemented method obtains a high PDR of 98% for 500 nodes, which is superior to the quantum behavior and gaussian mutation Archimedes optimization algorithm (QGAOA), with a delay of 15 ms for 100 nodes which is lesser than fuzzy and secured clustering algorithms. In comparison to the trust-based routing protocol for WSNs utilizing an adaptive genetic algorithm (TAGA), this implemented method achieves defense hello fold, black hole, sinkhole, and selective forwarding attacks effectively.
MetaLung: Meticulous affine-transformation-based lung cancer augmentation method
Authors: Nam Diana , Panina Alexandra , Pak Alexandr , Hajiyev Fuad
Published: July 2024

The limitation of medical image data in open source is a big challenge for medical image processing. Medical data is closed because of confidential and ethical issues, also manual labeling of medical data is an expensive process. We propose a new augmentation method named MetaLung (Meticulous affine-transformation-based lung cancer augmentation method) for lung CT image augmentation. The key feature of the proposed method is the ability to expand the training dataset while preserving clinical and instrumental features. MetaLung shows a stable increase in image segmentation quality for three CNN-based models with different computational complexity (U-Net, DeepLabV3, and MaskRCNN). Also, the method allows in reduce the number of False Positive predictions.
Cattle weight prediction model using convolutional neural network and artificial neural network
Authors: Yulianingsih Yulianingsih , Nurdiati Sri , Sukoco Heru , Sumantri Cece
Published: July 2024

The weight of livestock is a crucial metric for evaluating management efficacy, informing policy decisions, and determining the market value of animals. In certain scenarios, conventional methods such as physical weighing and measurement calculations can prove challenging, including the absence of livestock health records or weighing equipment. This research aims to develop a predictive model for estimating the live weight of cattle through visual assessments and metadata, including age and pixel count, utilizing a combination of convolutional neural network (CNN) and artificial neural network (ANN) methodologies. A total of 223 data were obtained from a local farm before augmentation. The model's predictive capability was successfully demonstrated, with its performance quantified by an average mean absolute percentage error (MAPE) of 10% on test data. This study demonstrates that through the combination of CNN and ANN, as well as optimal parameter tuning, efficient prediction of cattle weight can be achieved.
A review of machine vision pose measurement
Authors: Xiaoxiao Wang , Beng Ng Seng , O. K. Rahmat Rahmita Wirza , Sulaima Puteri Suhaiza
Published: July 2024

This review paper provides a comprehensive overview of machine vision pose measurement algorithms. The paper focuses on the state-of-the-art algorithms and their applications. The paper is structured as follows: the introduction in provides a brief overview of the field of machine vision pose measurement. Describes the commonly used algorithms for machine vision pose measurement. Discusses the factors that affect the accuracy and reliability of machine vision pose measurement algorithms. Summarizes the paper and provides future research directions. The paper highlights the need for more robust and accurate algorithms that can handle varying lighting conditions and occlusion. It also suggests that the integration of machine learning techniques may improve the performance of machine vision pose measurement algorithms. Overall, this review paper provides a comprehensive overview of machine vision pose measurement algorithms, their applications, and the factors that affect their accuracy and reliability. It provides a valuable resource for researchers and practitioners working in the field of computer vision.