bluuebunny/crossref_metadata_2025 huggingface dataset:
Graph-Based Deadlock Analysis and Prevention for Robust Intelligent Intersection Management
Authors: Lin Kai-En , Wang Kuan-Chun , Chen Yu-Heng , Lin Li-Heng , Lee Ying-Hua , Lin Chung-Wei , Jiang Iris Hui-Ru
Published: November 2023

Intersection management systems, with the assistance of vehicular networks and autonomous vehicles, have the potential to perform traffic control more precisely than contemporary signalized intersections. However, as infrastructural intersection management controllers do not directly activate motions of vehicles, it is possible that the vehicles fail to follow the instructions from controllers, undermining system properties such as deadlock-freeness and traffic performance. In this article, we consider a class of robustness issues, the time violations, which stem from possible discrepancies between scheduled orders and real executions. We refine a graph-based intersection model to build our theoretical foundations and analyze potential deadlocks and their resolvability. We develop solutions that mitigate negative effects of time violations. In particular, we propose a Robustness-Aware Greedy Scheduling algorithm for robust scheduling and evaluate the deadlock-free robustness of different intersection models and scheduling algorithms. Experimental results show that the Robustness-Aware Greedy Scheduling algorithm is able to significantly improve robustness and keep a good balance with traffic performance.
Dynamic Planning of Optimally Safe Lane-change Trajectory for Autonomous Driving on Multi-lane Highways Using a Fuzzy Logic–based Collision Estimator
Authors: Sharma Omveer , Sahoo N. C. , Puhan Niladri B.
Published: November 2023

The collision avoidance system in an autonomous vehicle, intended to address traffic safety issues, has a crucial function called collision estimation. It accomplishes this by identifying potential dangers and notifying the drivers in advance or by using autonomous control to navigate safely. In this work, a novel approach is proposed for generating and selecting a lane-change trajectory for the vehicle in a driving scenario where two vehicles are simultaneously executing lane-change processes on highways and approaching the same target lane. Moreover, a novel fuzzy logic estimator based on time-to-collision (TTC) and time-to-gap (TTG) is designed to estimate the collision risk. In the collision-avoidance process, the proposed estimator is utilized to determine the risk of a collision with polynomial function-based generation of possible lane-change trajectories. The safest lane-change trajectory is then provided to the motion controller so it can navigate the vehicle safely through such a challenging lane-change scenario. This work also investigates Stanley and Pure Pursuit controllers to follow the optimized trajectory. The simulation experiment results demonstrate that the proposed approach for dynamic trajectory generation during the lane-change process can successfully handle this type of challenging situation and prevent a potential collision. Experimental results also indicate that monitoring the movement of the nearby lane-changing vehicle is crucial for safe lane-change execution and that the proposed approach successfully handles the challenging situation, preventing potential collision.
Efficient Plutonium Extraction and Electrochemical Insights in a Hydrophobic Deep Eutectic Solvent for Radioactive Waste Management
Authors: Patil Sushil M. , Jayachandran Kavitha , Sahu Manjulata , Gupta Ruma
Published: November 2023

In this study, we explore the extraction and electrochemical characteristics of Plutonium using a hydrophobic deep eutectic solvent (DES) consisting of tetra-Butyl Ammonium Bromide(TBABr) as the hydrogen bond acceptor and Decanoic acid (DA) as the hydrogen bond donor in a 1:2 ratio composition. Hydrophobic DES possesses unique attributes, such as low volatility and a high affinity for metal ions, making it a promising choice for Plutonium extraction. We systematically investigate the influence of various parameters, including DES composition, acid concentration, and extraction duration. Our extraction experiments reveal that the hydrophobic DES exhibits its maximum Plutonium extraction efficiency (95%) at a 4 M HNO3 concentration, with a distribution coefficient (D) reaching nearly 71. To gain insights into the electrochemical behavior of Plutonium within the hydrophobic DES, we conduct cyclic voltammetry experiments. The resulting voltammograms offer valuable insights into the redox properties and stability of Plutonium species within the DES. Furthermore, we analyze the impact of potential scan rate on the electrochemical response, shedding light on the kinetics and mechanisms underlying Plutonium electrochemistry within the hydrophobic DES. These findings underscore the considerable potential of hydrophobic DES for efficient Plutonium extraction and electrochemical separation. This research contributes to the development of sustainable and environmentally friendly approaches for managing Plutonium, particularly in the context of nuclear waste disposal
Intestinal Ultrasound Combined with Blood Inflammatory Markers Is a More Efficient Tool in Evaluating Severity of Crohn’s Disease: A Pilot Study
Authors: Fang Huaying , Liu Jie , Qian Kai , Xu Xuemei , Li Zhaolong , Xie Li , Sun Menghan , Wang Song , Xu Jiaqin , Lv Chaolan , Wang Bo , Liu Weiyong , Song Gengqing , Yu Yue
Published: November 2023

Background and Aims. Intestinal ultrasound (IUS) is considered a nonirradiating, noninvasive, well-tolerated, and valuable tool for objectively assessing Crohn’s disease (CD) activity. However, there is no widely accepted intestinal ultrasound scoring system. This study is aimed at evaluating the efficacy of IUS key parameters, the International Bowel Ultrasound Activity Score (IBUS-SAS), and IBUS-SAS combined with blood inflammatory markers in assessing CD activity. Methods. 40 CD patients were reviewed in this retrospective study and were divided into the moderate-severe group (

n
=
25

) and nonmoderate-severe group (

n
=
15

) based on a simplified endoscopic score of Crohn’s disease (SES-CD). Double-balloon enteroscopy/colonoscopy were reviewed by three gastroenterologists. A transabdominal ultrasound was performed by two ultrasound specialists. Blood inflammatory markers were measured from morning samples. Results. In evaluating moderate to severe CD patients, (1) IBUS-SAS had a good predictive effect with an area-under-the-curve (AUC) of 0.855 (

P
<
0.001

); (2) IUS key parameters (including BWT, CDS, BWS, and I-fat) yielded good predictive effects with AUC of 0.811, 0.731, 0.724, and 0.747, respectively (

P
<
0.001

); (3) blood inflammatory markers (including ESR, CRP, PLR, MLR, and NLR) also had good predictive effects with AUC of 0.771, 0.837, 0.728, 0.743, and 0.775, respectively (

P
<
0.001

); (4) IBUS-SAS combined with ESR and CRP exerted the best predictive effect with the highest AUC of 0.912 (95% CI: 0.823-1.000), and the sensitivity and specificity were 88.0% and 80.0%, respectively (

P
<
0.001

). Conclusion. IBUS-SAS combined with ESR and CRP is a more efficient tool than IBUS-SAS alone or inflammatory markers alone in evaluating CD patients with moderate to severe disease activity.
Techno-Econo-Enviro Assessment of Photovoltaic-Thermal (PV-T) System for Residential Use in Iran Based on Köppen Climate Classification
Authors: Haghgoo Fakhr Mehdi , Rahimi Ariaei Afrooz , Jahangiri Mehdi
Published: November 2023

The reason for utilizing PV-T systems is their capability to generate both electricity and heat at the same time, which enhances energy efficiency and decreases the need for multiple energy sources. Considering the potential of solar energy and different climates in Iran, in this research, the feasibility, modeling, and comparison of the PV-T system to supply electricity and heat to residential units were investigated in 28 cities with 9 climates of Iran. A thorough evaluation of performance was conducted using the software Polysun 9.0 and PVsyst 6.6.8 for a duration of one year. The scientific novelty of this study relies on the simultaneous utilization of two analytical software, the thoroughness of the research, and taking into account the impact of climate on the findings. Investigations showed that the best performance of the designed system is related to Bandar Abbas and Zahedan, and the worst performance is related to Rasht and Sari. In general, according to the amount of electricity and heating produced by the PV-T system, it showed that Shiraz and Rasht stations have the highest and lowest production of energy. In terms of environmental effects, Birjand station will have the most and Rasht the least reduction in CO2 emissions. In terms of heating energy demand for the investigated building, Ardabil has the highest amount and Bandar Abbas has the lowest amount.
Loneliness during the COVID-19 Pandemic in Patients with Burning Mouth Syndrome: A Multicentric Case-Control Italian Study
Authors: Adamo Daniela , Ottaviani Giulia , Canfora Federica , Leuci Stefania , Coppola Noemi , Pecoraro Giuseppe , Rupel Katia , Preda Magdalena Theodora Bogdan , Vello Veronica , Albert Umberto , Gobbo Margherita , Guarda-Nardini Luca , Giudice Amerigo , Calabria Elena , Aria Massimo , D’Aniello Luca , Biasotto Matteo , Mignogna Michele Davide
Published: November 2023

Background. Lockdown restrictions during the COVID-19 pandemic exerted a strong impact on people’s quality of life and increased loneliness. This study evaluates the effect of the pandemic on loneliness in patients with burning mouth syndrome (BMS) compared with the general population. Methods. 100 BMS patients versus 100 healthy controls (HC) were recruited in five Italian centers. The 12-Item General Health Questionnaire (GHQ-12), the Depression Anxiety Stress Scales-21 (DASS-21), the Insomnia Severity Index (ISI), the short-form UCLA Loneliness Scale-8 (ULS-8), the Multidimensional Scale of Perceived Social Support (MSPSS), and the Suicidal Ideation Attribute Scale (SIDAS) were administered. Results. BMS patients and HC showed high scores (16 [14-20.25] and 16 [14-18]) in the ULS-8. Statistically significant differences have been found considering the BMS patients lived with fewer relatives during the lockdown compared with the HC (2 [2-3] and 3 [2-4];

p

: 0.012) with a lower level of satisfaction in relationships with relatives (4 [1.75-5] and 5 [4-5];

p
<
0.001

) and also in the DASS-21 total scores between the BMS patients and HC (16 [10-24.2] and 10 [4-17];

p
<
0.001

). The multivariate logistic regression revealed that age, education, DASS-21, and MSPSS were the most predictive variables and could explain 34.68% of the variance in the ULS-8 score (

p
<
0.001

) in the BMS group. However, only the DASS-21 was significant in the HC group, explaining 10.11% of the variance of the ULS-8 (

p

: 0.033). Conclusions. Both the patients and controls experienced deep loneliness during the pandemic. However, in the BMS group, loneliness was significantly correlated with age, a higher level of education and stress, and a lower level of satisfaction in relationships with relatives and social support perceived compared with the controls.
Nanomaterials in Nanophotonics Structure for Performing All-Optical 2 × 1 Multiplexer Based on Elliptical IMI-Plasmonic Waveguides
Authors: Mustafa Sajjad Mohanad , Karimi Gholamreza , Malek Shahi Mazdak Rad , Abdulnabi Saif Hasan
Published: November 2023

In this study, an all-optical multiplexer (Mux) based on elliptical insulator-metal-insulator (IMI) plasmonic waveguides is designed. The area of the proposed structure is very small (400 nm × 400 nm) which operates at a wavelength of 1,550 nm. The developed device utilizes constructive and destructive interferences between the input signals and the selector signal. This structure is less complex and has lower loss compared to the previous works. Transmission (T), contrast ratio (CR), modulation depth (MD), insertion loss (IL), and contrast loss (CL) are the five parameters that describe the performance of the plasmonic Mux. The transmission threshold between logic 0 and logic 1 is 0.5. Moreover, the maximum transmission efficiency of the device is 163%. Moreover, based on the MD value of 95.09%, the dimensions of the proposed structure are excellent and optimal. The proposed plasmonic Mux structure contributes substantially to developing an all-optical arithmetic logic unit (ALU) and all-optical signal processing nanocircuits. The finite element method (FEM) simulates the proposed plasmonic multiplexer with COMSOL Multiphysics 5.4 software.
Reduced switch cascaded asymmetrical 27 level inverter-STATCOMwith fuzzy logic controller
Authors: Ramesh Sundar , Govindaraj Vijayakumar , Raman Raja , Venkatarajan Shanmugasundaram , Kannan Vijayarangan Kamatchi
Published: November 2023

In this study, a 27-levelinverter with a reduced switch asymmetrical cascaded H-bridge (CHB) with fuzzy logic controller (FLC) is proposed. With series connections, a low voltage converter, a middle level voltage converter, and a high voltage converter make up the static synchronous compensator (STATCOM). The configuration of the asymmetrical inverter uses trinary DCsources. To acquire switching signals for the trinary inverter-based STATCOM to compensate for real power, load voltage, reactive power, load current, and power factor under load changing conditions, FLC is constructed. With fewer switches, the suggested arrangement produces greater voltage levels. The performance of the reduced switch asymmetrical cascaded H-bridge inverter-STATCOM with FLCis simulated using the MATLAB Simulink platform under both static and dynamic load conditions. When compared to reduced switch asymmetrical cascaded H-bridge inverter-STATCOM with traditional proportional integral (PI) controller, the FLC result demonstrates efficient unbalanced load compensation. The FLC in the proposed inverter also lowers the total harmonic distortion.
Independent swarm grid vehicle charging by a logical DC-DC converter with hybrid boundary conduction mode
Authors: Nagarajan Karthikeyan , Esakkiappan Kaliappan , Kala Pandian Dinakaran , Ramasamy Dharmaprakash
Published: November 2023

A modern and expanding civilization now takes for granted that future environments will be clean, efficient, and carbon neutral. Energy-efficient electrical loads, such as v ehicle charging now often u sed in schools, companies, and gated communities. Recent articles have focused more on Swarm smart grid technology as a way to transmit power from rooftop solar installations. A major issue with the system is the necessity for inverters and other conversio n equipment. The mechanism consumes part of the power generated by this process. It is possible to utilize the DC supply to power the v ehicle chargers in smart houses, although some tweaks may be necessary. The charging Vehicle is a common task that can sh are by different converter by swarm network. This study demonstrates the feasibility of using a swam nano grid t o interconnect buildings inside a campus or other large business or residential complex, as well as within a gated community. The electricity gr id is monitored by the swam nano grid system, which eliminates the need for costly equipment. The DC - DC converter was being controlled directly by the simple logic controller. M ATLAB Simulink simulates the controlled converter under load and shows the performance of a bidirectional DC-DC converter using waveforms.
Mathematical and computer modeling of atmospheric air pollutants transformation with input data refinement
Authors: Temirbekov Nurlan , Malgazhdarov Yerzhan , Tamabay Dinara , Temirbekov Almas
Published: November 2023

This article addresses the critical issue of harmful impurity dispersion in industrial facility atmospheres, considering point sources and factoring in photochemical changes. Conjugate equations are employed to assimilate pollutant data into the transfer equations' right side. Boundary conditions derive from global models like weather research and forecasting (WRF) and system for integrated modeling of atmospheric composition (SILAM), customized for the unique characteristics of an industrial city's pollutants. To encompass anthropogenic heat sources and surface heterogeneity, the model incorporates differential schemes for the atmospheric boundary layer, transport equations, and impurity transformation equations. Parameters for photochemical transformations, varying with weather and time of day, are derived from Ust-Kamenogorsk. A cloud-based geoinformation system (GIS) is developed for monitoring and forecasting air pollution. It assimilates data sources and accounts for photochemical transformations, enabling visualization of diverse weather and environmental scenarios. The article presents numerical modeling results of impurity spread and transformation influenced by mesometeorological processes, topography, and water resources within a specific city.
5G handover issues and techniques for vehicular communications
Authors: Ali Zuhdi Rosli Muhamad , Fatimah Abdul Razak Siti , Yogarayan Sumendra
Published: November 2023

Vehicular communication is gaining popularity, and seamless handover is critical to maintaining a stable and uninterrupted network connection between vehicles and roadside units. This paper investigates the advancements in handover approaches in vehicular networks, with a specific focus on 5G technology. Vehicular Ad-hoc Networks (VANETs) face challenges due to high mobility, dynamic network topology, and frequent information exchange. The paper discusses handover issues in 5G VANET environments, such as too-late and too-early handovers, wrong handover decisions, and unnecessary handovers. It also explores key performance indicators (KPIs) used in handover evaluation. The advancements in handover approaches presented in this paper pave the way for enhanced connectivity and communication management in 5G VANETs, contributing to the development of safer and more efficient intelligent transportation systems.
Machine learning based detection of DDoS attacks in software defined network
Authors: Kannan Charulatha , Muthusamy Rajendiran , Srinivasan Vimala , Chidambaram Vivek , Karunakaran Kiruthika
Published: November 2023

Nowadays, software defined networking (SDN) offers benefits in the area so fautomation, elasticity, and resource consumption. However, evidenceis there that SDN controller may undergo certain defeat for the network structure, particularly as the yare targeted by attacks like denial of service (DoS). Due to this network traffic has increased tremendously and attacked the server severely. To handle this issue, weused the Ryu controller and Mininet tool to identify and all eviate the DoS attack by the machine learning (ML) algorithm. Since ML is deemed as themain method for detecting peculiarities, the detection of DoS attacks was done through ML based classification. In this paper, several ML techniques were used to identify the DoS attack, and the traffic which is causing the attack has been dropped immediately to avoid congestion. The proposed work hasbeen simulated in Mininet and the results show that the proposed work detects DoS attacks well and achieves good accuracy.
Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm
Authors: Ravinder Ravinder , Vurukonda Naresh
Published: November 2023

The cloud computing trend on the internet is vital as it allows data and applications to be managed over the internet instead of requiring personal devices. The job of users is scheduled in the resources of the cloud in order to improve performance. Schedu ling tasks is an non - deterministic polynomial (NP) - hard problem, as it may have multiple solutions. Various researchers have proposed different load balancing and job scheduling algorithms to optimize the scheduling process in cloud environments, each with disadvantages. Therefore, this research proposes a novel hybrid load balancing and scheduling of tasks by the whale optimization algo rithm (WOA) and seagull optimization algorithm (SOA) in the cloud. This hybrid proposed whale - seagull optimization algorithm (WSOA) optimizes task scheduling in the cloud b y reducing processing time, response, and execution time, maximizing central processing unit (CPU) utilization, memory utilization, throughput, reliability, and balancing the load. The algorithm is simulated using the CloudSim toolkit package. As compared with existing approaches, simulation results showed better performance in terms of response time, processing time, execution time, CPU utilization, memory utilization, throughput, and reliability and is analyzed by comparing with the harries hawks optimiza tion (HHO), hybrid dragonfly and firefly algorithm (ADA), spider monkey algorithm (SMA) and bird swarm optimization (BSO).
Intelligent photovoltaic system to maximize the capture of solar energy
Authors: Ovalle Paulino Christian , Rojas Nieves Luis , Villaverde Medrano Hugo , Paiva Peredo Ernesto
Published: November 2023

The large consumption of electricity worldwide has an impact on the environment which can be said to alter climate change, the degradation of the ozone layer and acid rain. A house has an average daily consumption of 270kWh, this is why solar panels are very useful and can help to have a renewable energy at low costs. To create an intelligent photovoltaic system, different electronic sensors can be applied to follow the sunlight through a series of instructions in some programming software. The article proposes to prototype an intelligent photovoltaic system, based on artificial intelligence with a neural network library "propet" having a positive impact on the optimization of power generation by allowing a more accurate tracking of the sun and a greater collection of photovoltaic energy throughout the day. performing an integration between Arduino and machine learning algorithms such as artificial neural networks in prediction of time series. Different practical experiments were performed to illustrate the effectiveness of the proposed method.
Cybersecurity awareness among university students in Mogadishu: a comparative study
Authors: Abdukadir Ahmed Adnan , Hussein Elmi Abdikadir , Abdullahi Abdijalil , Yahye Ahmed Abdullahi
Published: November 2023

This study aimed to assess the level of cyber security awareness among graduate and undergraduate students in five universities in Mogadishu. The study used a one-way analysis of variance (ANOVA) to examine the difference in cyber security awareness levels between graduate and undergraduate students across five reputable universities. The questionnaire method was used to collect data from 250 graduate and undergraduate students from SIMAD, SIU, UNISO, Jamhuriya, and Mogadishu universities. The cross-tabulation result showed that there was a significant difference in cyber security awareness levels between the universities. Specifically, the results showed that students from SIMAD and Jamhuriya universities suffered from virus attacks, while SIU students struggled with password strength and social network misuse. Mogadishu students faced phishing and virus attacks, and UNISO students dealt with both virus attacks and password strength issues. The study recommended that universities educate their students and parents on safe internet usage and cybersecurity and monitor and secure their internet and computer services. Additionally, the authors recommended the development of cybersecurity software to help students use their data confidently and securely.
Optimal capacity threshold for reversible watermarking using score function
Authors: Panyindee Chaiyaporn
Published: November 2023

Histogram shifting is an important technique of reversible watermarking, which can embed large payloads into digital images with low distortion. The technique must determine two threshold values to achieve the lowest possible distortion. Appropriate threshold values might be found by iterative methods, but it is computationally inefficient when the payloads are high and varied. In this paper, we show that the optimal threshold values lie on a straight line and occur at the boundary of the payload-satisfying region. Moreover, we propose a high performance algorithm to approximate the optimal threshold values. Under the same image quality, experimental results indicate that the proposed scheme could get closer threshold values to the optimal threshold values, compared to previous work. Therefore, it requires a smaller number of iterations to obtain the desirable threshold values.
Detecting fake news spreaders on twitter
Authors: Ali Saber Ali , Khalil Easa Haider , Raoof Ismael Arkan , Ali Saber Hindren , Kamaran Omer Aso
Published: November 2023

Nowadays, fake news is prevalent and too simple to propagate through social media, particularly during elections and pandemics like COVID-19. Several fake news stories have appeared on social media sites like LINE, Facebook, and Twitter after the COVID-19 epidemic throughout the world. Also, a lot of older individuals simply forward these communications without checking their veracity, which speeds up the dissemination of fake information. So, our goal is to identify fake news using machine learning. In this paper, we describe a supervised method that automatically gathers a sizable but noisy training dataset made up of a significant number of tweets. We will categorize tweets during collection into trustworthy and untrustworthy sources, then using the dataset to train a classifier. The categorization of fake and real tweets is the next classification objective for which we apply that classifier. We first demonstrate that real news is larger in size, shared on Twitter for a longer length of time, and shared by people with more followers than following. Second, we employed machine learning models like support vector machine (SVM), random forest (RF), and decision tree (DT), and we found out that the SVM is the best of all the models due to its best results and 99% accuracy.
Energy aware reliable routing model for sensor network enabled internet of things environment
Authors: Mysuru Srikantha Padmini , Kuzhalvaimozhi Sampath , Mallikarjun Silli Samaresh , Prakash Suraj , Verma Tanay , Manjunatha Varun
Published: November 2023

Wireless sensor networks (WSNs), which are facilitated by the internet of things (IoTs), can be difficult to improve the lifespan of the network target area. Although the hotspot issue (i.e., the cluster head closest to the base station fails quickly) is mitigated by the clustered-based routing technique, it still has an important effect on the network's lifespan and target area. However, improper distribution of load between cluster heads has been shown to negatively impact network lifespan efficiency, so even though unequal clusters have been utilized successfully to tackle the hotspot issue, further work is needed. This study provides an energy-aware reliable routing (EARR) model for resolving the hotspot as well as load balancing issues simultaneously. To extend the lifespan of the network, the EARR model effectively minimizes energy consumption by the cluster heads using enhanced multi-objective optimization parameters. Further, EARR provides improved routing optimization metrics to improve data delivery with energy efficiency, less delay, and packet loss. The results of the experiments demonstrate that the EARR model provides excellent throughput and lifespan efficiency with low delay and communication overhead.
Accuracy enhancement with artificial neural networks for bipolar disorder prediction
Authors: Agnihotri Nisha , Kumar Prasad Sanjeev
Published: November 2023

The perfect physical health and mental wellbeing is an important aspect of human kind. Healthcare sectors involving machine learning and deep learning is providing good healthcare services is helping people for safeguarding them from being exploited with extra and unnecessary expenditures on medical check-ups. This gives treatments and many health services on time when needed. In this paper, different performance metrics are applied on online bipolar dataset named “Theory of mind in remitted bipolar disorder dataset” from Kaggle to evaluate the diagnosis for bipolar disorder feature prediction and analysis. In this study the proposed accuracy is better as compared to previous traditional models. As a result, artificial neural networks reduce the time taken in training and classification of dataset in prediction as given in result by optimal combination of epoch and hyperperameters.
Diabetes mellitus prediction using machine learning within the scope of a generic framework
Authors: Arora Nidhi , Srivastava Shilpa , Agarwal Ritu , Mehndiratta Vandana , Tripathi Aprna
Published: November 2023

Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models.
Global software development agile planning model: challenges and current trends
Authors: Lamsellak Hajar , Ghaouth Belkasmi Mohammed
Published: November 2023

Agile planning offers a number of benefits that make the customers active members of the team throughout the project. In global software development (GSD), geographic separations demand special attention to harness these benefits. Our paper conducted a systematic mapping study (SMS) to analyze GSD-specific agile planning challenges followed by a systematic literature review (SLR) for efficient solutions. These studies led to a model for agile planning in global software development supporting GSD practitioners during this process.
Effect of Recombinant Human Platelet-Derived Growth Factor on Healing of Chronic Periapical Tissue Pathosis Following Apical Surgery in a Canine Model: A Histomorphometric and Microcomputed Tomography Analysis
Authors: Al Hezaimi Khalid , Naghshbandi Jafar , Rotstein Ilan
Published: November 2023

This canine in vivo study assessed the effect of recombinant human platelet-derived growth factor (rhPDGF) on the healing of periapical tissues following apical surgery. From a total of 96 premolar teeth, 64 teeth from six beagle dogs (2 years old) were classified as experimental and were randomly assigned to four experimental groups (16 teeth per group). After having the pulp extirpated, leaving teeth open to the oral cavity for 1 week, and sealing with an immediate restorative material for 8 weeks, nonsurgical endodontic treatment was performed. A split-mouth design was used, and intra-animal randomization of treatment sides was applied to the groups as follows: apical curettage + 1.5-mm root-end resection (Group 1); apicoectomy + mineral trioxide aggregate (MTA) root-end filling (Group 2); apicoectomy + MTA root-end filling + rhPDGF (Group 3); and apical curettage + rhPDGF (Group 4). The animals were sacrificed 24 months after apical surgery, and histologic and μCT analyses were performed for bone volume loss (BVL). Group 1 showed partial resolution of the periapical lesions without signs of tissue regeneration (BVL: 49.09 ± 10.97 mm3). Group 2 had minimal bone regeneration and showed cementum reformation in 9 teeth, with no direct attachment to the MTA (BVL: 35.34 ± 10.97 mm3). Group 3 showed regeneration of all damaged apical tissues without direct contact between the cementum and MTA (BLV: 4.51 ± 1.55 mm3). Group 4 showed regeneration of PDL, bone, and cementum and attachment of functional cementum fibers (BVL: 2.82 ± 2.3 mm3). The difference in BVL was statistically significant only for Groups 1 and 2 (P < .05). rhPDGF may help regenerate apical tissue structures following apical surgery.
Major Facilitator Superfamily Domain Containing 5 Inhibition Reduces Lipoprotein(a) Uptake and Calcification in Valvular Heart Disease
Authors: Rogers Maximillian A. , Bartoli-Leonard Francesca , Zheng Kang H. , Small Aeron M. , Chen Hao Yu , Clift Cassandra L. , Asano Takaharu , Kuraoka Shiori , Blaser Mark C. , Perez Katelyn A. , Natarajan Pradeep , Yeang Calvin , Stroes Erik S.G. , Tsimikas Sotirios , Engert James C. , Thanassoulis George , O’Donnell Christopher J. , Aikawa Masanori , Singh Sasha A. , Aikawa Elena
Published: November 2023

BACKGROUND:

High circulating levels of Lp(a) (lipoprotein[a]) increase the risk of atherosclerosis and calcific aortic valve disease, affecting millions of patients worldwide. Although atherosclerosis is commonly treated with low-density lipoprotein–targeting therapies, these do not reduce Lp(a) or risk of calcific aortic valve disease, which has no available drug therapies. Targeting Lp(a) production and catabolism may provide therapeutic benefit, but little is known about Lp(a) cellular uptake.

METHODS:

Here, unbiased ligand-receptor capture mass spectrometry was used to identify MFSD5 (major facilitator superfamily domain containing 5) as a novel receptor/cofactor involved in Lp(a) uptake.

RESULTS:

Reducing
MFSD5
expression by a computationally identified small molecule or small interfering RNA suppressed Lp(a) uptake and calcification in primary human valvular endothelial and interstitial cells.
MFSD5
variants were associated with aortic stenosis (
P
=0.027 after multiple hypothesis testing) with evidence suggestive of an interaction with plasma Lp(a) levels.

CONCLUSIONS:

MFSD5
knockdown suppressing human valvular cell Lp(a) uptake and calcification, along with meta-analysis of
MFSD5
variants associating with aortic stenosis, supports further preclinical assessment of
MFSD5
in cardiovascular diseases, the leading cause of death worldwide.
The Qurʾān and Knowledge of God in West Africa: The Sufi Tafsīr of Shaykh Ibrāhīm Niasse
Authors: Wright Zachary
Published: November 2023

Qurʾān exegesis (tafsīr) in African Muslim societies represented the pinnacle of scholarly achievement, and public explanation of the Qurʾān was the event that marked the emergence of one of Africa’s most successful Sufi revivals, the “Community of the Flood” of the Senegalese Shaykh Ibrāhīm Niasse (d. 1975). Niasse’s network of knowledge transmission, foregrounding the direct experiential knowledge of God (maʿrifa bi-Llāh), continued to emphasize Qurʾān learning, but Niasse’s own recorded Arabic tafsīr demonstrated a shift away from traditional West African sources in this field. Prior understandings of the West African tafsīr discipline locate the fifteenth-century Egyptian Tafsīr al-Jalālayn as the primary influence on West African understandings. But Niasse’s tafsīr exhibits a clear preference for an early eighteenth-century Ottoman multivolume work, Ismāʿīl Ḥaqqī’s “Spirit of Explanation” (Rūh al-bayān), one of the most comprehensive summaries of Sufi understandings of the Qurʾān. This paper not only demonstrates the globally-connected nature of Islamic knowledge production in West Africa but also argues that Niasse’s emphasis on gnosis built on the Rūḥ al-bayān to ultimately occasion a noteworthy addition to the existing literary corpus of Qurʾān exegesis.
Checklist of Bryophytes (Anthocerotophyta, Bryophyta and Marchantiophyta) of the state of Maranhão, Brazil
Authors: OLIVEIRA RONISON FERREIRA , DA SILVA GUILHERME SOUSA , RODRIGUES DE OLIVEIRA REGIGLÁUCIA , MARCHI DO CARMO DIMAS , FERNANDES PERALTA DENILSON , MENDES DA CONCEIÇÃO GONÇALO
Published: November 2023

The number of bryophyte species from Maranhão has increased considerably in recent years due to new studies, with the frequent publication of new occurrences for the state. Effort is needed to organize and update the names to create an updated list of bryophyte taxa for the state of Maranhão based on published bibliographic surveys and reanalysis of collections. Here we provide an updated checklist of bryophytes of Maranhão as well as an assessment of the richness and taxonomic composition of the species and their phytogeographic distribution in relation to other Brazilian states. A total of 3727 collection records were analyzed, resulting in 269 species distributed in 110 genera and 43 families. The most representative division was Bryophyta with 151 species, the richest family was Lejeuneaceae (70 spp.), and the richest genus was Fissidens (24 spp.). One species—Ceratolejeunea maranhensis, from the Amazon domain—is endemic to Maranhão and eight are endemic to Brazil. The number of species found in this study places Maranhão in the thirteenth position among the Brazilian states in terms of diversity of bryophytes, revealing the importance of Maranhão as a center for the study and conservation of Brazilian bryofloristic diversity.
Bulbophylum sect. Cirhopetallum s. str. (Orchidaceae) in Vietnam with the description of a new species, B. thangii, and a new national record, B. fenestratum
Authors: TRUONG BA VUONG , AVERYANOV LEONID V. , MAISAK TATIANA V. , DANG MINH QUAN , NGUYEN VAN CANH , AMLSER ROLAND , NGUYEN QUOC BAO , DANG VAN SON
Published: November 2023

The taxonomical survey of Bulbophyllum sect. Cirhopetallum s. str. with the key for identification of all four species occurring in Vietnam is presented. Bulbophylum thangii, belonging to this section and discovered in Khanh Hoa Province, southern Vietnam, is described as a new species for science. The new species shares prominent tooth on the inner side of stelidia with all species in its section but differs from them in warty or colliculate sepals and lip. Bulophyllum fenestratum from the same section is reported from Dong Nai Province as a new national record for the orchid flora of Vietnam. All species in the section are illustrated by plates with analytical color photos. Data on types, habitat, phenology, distribution, taxonomic remarks, and lists of studied specimens are reported for all species as well.
New record of two species of munidid squat lobster (Decapoda: Anomura) from the North-West Pacific off Japan
Authors: KOMAI TOMOYUKI , TSUCHIDA SHINJI , FUJIWARA YOSHIHIRO
Published: November 2023

Two species of munidid squat lobsters are newly reported from the North-West Pacific off Japan based on material collected by the recent cruise of R/V Kaimei (KM20-10C cruise) of the Japan Agency for Marine-Earth Science and Technology, exploring seamounts in the marine protected area (MPA): Gonionida kaimei n. sp. and Trapezionida psylla (Macpherson, 1994). Gonionida kaimei n. sp. closely resembles G. tangaroa (Ahyong, 2007) and G. rubrimana (Ahyong, 2007), but morphological characters and living colouration differentiate the new species from the latter two species. Genetic analysis using two molecular markers (mitochondrial COI and 16S rRNA genes) also supports the recognition of the new species. Trapezionida psylla has been recorded from the South-West Pacific, and thus the present material greatly extends the geographic range of the species to the North-West Pacific. The identity of the present specimens is also confirmed by the genetic data, as well as the morphological evidence. This study contributes to documenting the benthic invertebrate fauna of the Japanese MPA.
Does living alone mean being alone? Personal networks of solo-living adults in midlife
Authors: Kersten Philipp , Mund Marcus , Neyer Franz J.
Published: November 2023

People living alone are often depicted as prone to social isolation and poor well-being. Since previous research largely focused on comparisons between the living arrangements of older adults, evidence on differences within middle-aged adults living alone remains sparse. The present study used a person-centered approach to allow for a comprehensive understanding of the heterogeneity of living alone in midlife. Middle-aged adults ( N = 389; aged 35–60 years) reported on their personal networks, personality, and well-being and completed diaries on daily network interactions. Using latent class analysis, we constructed a network typology and examined differences in social motives, well-being, daily interaction quantity, and daily relationship quality. We found four structural network types: Individuals with diverse networks (highest in size, contact, proximity, and heterogeneity) felt less lonely than individuals with restricted (lowest in size, contact, proximity, and heterogeneity) or loose-knit networks (low in contact). Individuals with loose-knit networks reported poorer well-being than those in diverse or partner-focused networks (all partnered). We found little support for differences in social motives. All network types differed in daily interaction quantity but did not differ in daily relationship quality. The study highlights the heterogeneity of personal networks in middle-aged adults living alone. Possible implications for the social embeddedness and psychological adaptation of people living alone in midlife are discussed.
Smart library themes and elements: A systematic literature review
Authors: Yunus Norhazura , Nasir Ismail Mohd , Osman Ghazali
Published: November 2023

In recent years, libraries worldwide have been pushing to undergo drastic changes. These transformations impact library functions from traditional to electronic, digital, hybrid, virtual and smart libraries. However, there need to be more studies that systematically examine the elements that contribute to a smart library. Therefore, this article conducted a systematic literature review on the elements contributing to the smart library as a whole rather than focusing on one particular aspect. This study aims to explore the concept of smart libraries, including their essential elements, characteristics and theoretical foundations, to contribute to a deeper understanding of their role in transforming libraries. The review for this study was based on the publication standard and integrated multiple research designs using the review protocol RepOrting standards for Systematic Evidence Syntheses (ROSES). For this study, articles were selected from two major databases, Scopus and Web of Science, and a supporting database, Google Scholar. In these databases, 19 related studies were found on elements that comprise a smart library. The study’s search strings excluded publication years to focus on identifying discussions about smart library elements. Results indicated publication dates spanning 2018–2022. Six major themes emerged from the thematic analysis of this review, including smart technology, smart services, smart people, smart governance, smart places and smart buildings. The study makes several significant contributions to practical purposes and the body of knowledge. The results explain how the elements can contribute to a smart library. The influence of ethical values in smart library elements and their contribution to library sustainability should be the subject of future research.
Active lupus in Argentina: Results of a multicenter and national registry
Authors: Quintana Rosana M , García Mercedes , Garcia Lucila , Gobbi Carla , Alba Paula , Bellomio Verónica , Roverano Susana , Alvarez Analia Patricia , Graf Cesar Enrique , Pisoni Cecilia , Spindler Alberto , Gomez Catalina , Figueredo Heber Matias , Papasidero Silvia , Paniego Raul , de la Vega Maria Celina , Civit Emma , Gonzalez Lucero Luciana , Martire Maria Victoria , Aguila Maldonado Rodrigo , Gordon Sergio , Micelli Marina , Nieto Romina , Rausch Gretel , Alarcón Graciela S , Pons-Estel Bernardo , Pons-Estel Guillermo ,   on behalf of the SLE Study Group from the RELESSAR Registry
Published: November 2023

Objective

To evaluate the association between patients’ characteristics and disease activity in an Argentine lupus registry.

Methods

Cross-sectional study. Disease activity was stratified into: Remission off-treatment: SLEDAI = 0, without prednisone and immunosuppressive drugs. Low disease activity Toronto Cohort (LDA-TC): SLEDAI ≤2, without prednisone or immunosuppressive drugs. Modified lupus low disease activity (mLLDAS): SLEDAI score of ≤4, with no activity in major organ systems and no new features, prednisone of ≤10 mg/day and/or immunosuppressive drugs (maintenance dose) and Active disease: SLEDAI score of >4 and prednisone >10 mg/day and immunosuppressive drugs. A descriptive analysis and logistic regression model were performed.

Results

A total of 1346 patients were included. Of them, 1.6% achieved remission off steroids, 0.8% LDA-TC, 12.1% mLLDAS and the remaining 85.4% had active disease. Active disease was associated with younger age ( p ≤ 0.001), a shorter time to diagnosis ( p ≤ 0.001), higher frequency of hospitalizations ( p ≤ 0.001), seizures ( p = 0.022), serosal disease ( p ≤ 0.001), nephritis ( p ≤ 0.001), higher SDI ( p ≤ 0.001), greater use of immunosuppressive therapies and higher doses of prednisone compared to those on mLLDAS. In the multivariable analysis, the variables associated with active disease were the presence of pleuritis (OR 2.1, 95% CI 1.2–3.9; p = 0.007), persistent proteinuria (OR 2.5, 95% CI 1.2–5.5; p ≤ 0.011), nephritis (OR 2.5, 95% CI 1.2–5.6; p = .018) and hospitalizations (OR 8.9, 95% CI 5.3–16.0; p ≤ 0.001) whereas age at entry into the registry was negatively associated with it (OR 0.9, 95% CI 0.9–1.0; p = 0.029).

Conclusion

Active disease was associated with shorter time to diagnosis, worse outcomes (SDI and hospitalizations) and renal, neurological and serosal disease.
A support vector machine-based cure rate model for interval censored data
Authors: Pal Suvra , Peng Yingwei , Aselisewine Wisdom , Barui Sandip
Published: November 2023

The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic function. This readily implies that the boundary classifying the cured and uncured subjects is linear. In this article, we propose a new mixture cure rate model based on interval censored data that uses the support vector machine to model the effect of covariates on the uncured or the cured probability (i.e. on the incidence part of the model). Our proposed model inherits the features of the support vector machine and provides flexibility to capture classification boundaries that are nonlinear and more complex. The latency part is modeled by a proportional hazards structure with an unspecified baseline hazard function. We develop an estimation procedure based on the expectation maximization algorithm to estimate the cured/uncured probability and the latency model parameters. Our simulation study results show that the proposed model performs better in capturing complex classification boundaries when compared to both logistic regression-based and spline regression-based mixture cure rate models. We also show that our model’s ability to capture complex classification boundaries improve the estimation results corresponding to the latency part of the model. For illustrative purpose, we present our analysis by applying the proposed methodology to the NASA’s Hypobaric Decompression Sickness Database.