bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart Water Quality Monitoring with IoT Wireless Sensor Networks
Authors: Singh Yurav , Walingo Tom
Published: May 2024

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.
Technology Selection for Inline Topography Measurement with Rover-Borne Laser Spectrometers
Authors: Ryan Conor , Haist Tobias , Laskin Gennadii , Schröder Susanne , Reichelt Stephan
Published: May 2024

This work studies enhancing the capabilities of compact laser spectroscopes integrated into space-exploration rovers by adding 3D topography measurement techniques. Laser spectroscopy enables the in situ analysis of sample composition, aiding in the understanding of the geological history of extraterrestrial bodies. To complement spectroscopic data, the inclusion of 3D imaging is proposed to provide unprecedented contextual information. The morphological information aids material characterization and hence the constraining of rock and mineral histories. Assigning height information to lateral pixels creates topographies, which offer a more complete spatial dataset than contextual 2D imaging. To aid the integration of 3D measurement into future proposals for rover-based laser spectrometers, the relevant scientific, rover, and sample constraints are outlined. The candidate 3D technologies are discussed, and estimates of performance, weight, and power consumptions guide the down-selection process in three application examples. Technology choice is discussed from different perspectives. Inline microscopic fringe-projection profilometry, incoherent digital holography, and multiwavelength digital holography are found to be promising candidates for further development.
Indoor Navigation in Facilities with Repetitive Structures
Authors: Volkovich Zeev , Ravve Elena V. , Avros Renata
Published: May 2024

Most facilities are structured in a repetitive manner. In this paper, we propose an algorithm and its partial implementation for a cellular guide in such facilities without GPS use. The complete system is based on iBeacons-like components, which operate on BLE technology, and their integration into a navigation application. We assume that the user’s location is determined with sufficient accuracy. Our main goal revolves around leveraging the repetitive structure of the given facility to optimize navigation in terms of storage requirements, energy efficiency in the cellular device, algorithmic complexity, and other aspects. To the best of our knowledge, there is no prior experience in addressing this specific aim. In order to provide high performance in real time, we rely on optimal saving and the use of pre-calculated and stored navigation sub-routes. Our implementation seamlessly integrates iBeacon communications, a pre-defined indoor map, diverse data structures for efficient information storage, and a user interface, all working cohesively under a single supervision. Each module can be considered, developed, and improved independently. The approach is mainly directed to places, such as passenger ships, hotels, colleges, and so on. Because of the fact that there are “replicated” parts on different floors, stored once and used for multiple routes, we reduce the amount of information that must be stored, thus helping to reduce memory usage and as a result, yielding a better running time and energy consumption.
Applications of Chipless RFID Humidity Sensors to Smart Packaging Solutions
Authors: Mulloni Viviana , Marchi Giada , Gaiardo Andrea , Valt Matteo , Donelli Massimo , Lorenzelli Leandro
Published: May 2024

Packaging solutions have recently evolved to become smart and intelligent thanks to technologies such as RFID tracking and communication systems, but the integration of sensing functionality in these systems is still under active development. In this paper, chipless RFID humidity sensors suitable for smart packaging are proposed together with a novel strategy to tune their performances and their operating range. The sensors are flexible, fast, low-cost and easy to fabricate and can be read wirelessly. The sensitivity and the humidity range where they can be used are adjustable by changing one of the sensor’s structural parameters. Moreover, these sensors are proposed as double parameter sensors, using both the frequency shift and the intensity variation of the resonance peak for the measure of the relative humidity. The results show that the sensitivity can vary remarkably among the sensors proposed, together with the operative range. The sensor suitability in two specific smart packaging applications is discussed. In the first case, a threshold sensor in the low-humidity range for package integrity verification is analyzed, and in the second case, a more complex measurement of humidity in non-hermetic packages is investigated. The discussion shows that the sensor configuration can easily be adapted to the different application needs.
An Adaptive Trust Evaluation Model for Detecting Abnormal Nodes in Underwater Acoustic Sensor Networks
Authors: Liu Changtao , Ye Jun , An Fanglin , Jiang Weili
Published: May 2024

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.
Intra and Inter-Device Reliabilities of the Instrumented Timed-Up and Go Test Using Smartphones in Young Adult Population
Authors: dos Santos Thâmela Thaís Santos , Marques Amélia Pasqual , Monteiro Luis Carlos Pereira , Santos Enzo Gabriel da Rocha , Pinto Gustavo Henrique Lima , Belgamo Anderson , Costa e Silva Anselmo de Athayde , Cabral André dos Santos , Kuliś Szymon , Gajewski Jan , Souza Givago Silva , Silva Tacyla Jesus da , Costa Wesley Thyago Alves da , Salomão Railson Cruz , Callegari Bianca
Published: May 2024

The Timed-Up and Go (TUG) test is widely utilized by healthcare professionals for assessing fall risk and mobility due to its practicality. Currently, test results are based solely on execution time, but integrating technological devices into the test can provide additional information to enhance result accuracy. This study aimed to assess the reliability of smartphone-based instrumented TUG (iTUG) parameters. We conducted evaluations of intra- and inter-device reliabilities, hypothesizing that iTUG parameters would be replicable across all experiments. A total of 30 individuals participated in Experiment A to assess intra-device reliability, while Experiment B involved 15 individuals to evaluate inter-device reliability. The smartphone was securely attached to participants’ bodies at the lumbar spine level between the L3 and L5 vertebrae. In Experiment A, subjects performed the TUG test three times using the same device, with a 5 min interval between each trial. Experiment B required participants to perform three trials using different devices, with the same time interval between trials. Comparing stopwatch and smartphone measurements in Experiment A, no significant differences in test duration were found between the two devices. A perfect correlation and Bland–Altman analysis indicated good agreement between devices. Intra-device reliability analysis in Experiment A revealed significant reliability in nine out of eleven variables, with four variables showing excellent reliability and five showing moderate to high reliability. In Experiment B, inter-device reliability was observed among different smartphone devices, with nine out of eleven variables demonstrating significant reliability. Notable differences were found in angular velocity peak at the first and second turns between specific devices, emphasizing the importance of considering device variations in inertial measurements. Hence, smartphone inertial sensors present a valid, applicable, and feasible alternative for TUG assessment.
Smart Textile Impact Sensor for e-Helmet to Measure Head Injury
Authors: Saikia Manob Jyoti , Alkhader Arar Salim
Published: May 2024

Concussions, a prevalent public health concern in the United States, often result from mild traumatic brain injuries (mTBI), notably in sports such as American football. There is limited exploration of smart-textile-based sensors for measuring the head impacts associated with concussions in sports and recreational activities. In this paper, we describe the development and construction of a smart textile impact sensor (STIS) and validate STIS functionality under high magnitude impacts. This STIS can be inserted into helmet cushioning to determine head impact force. The designed 2 × 2 STIS matrix is composed of a number of material layered structures, with a sensing surface made of semiconducting polymer composite (SPC). The SPC dimension was modified in the design iteration to increase sensor range, responsiveness, and linearity. This was to be applicable in high impact situations. A microcontroller board with a biasing circuit was used to interface the STIS and read the sensor’s response. A pendulum test setup was constructed to evaluate various STISs with impact forces. A camera and Tracker software were used to monitor the pendulum swing. The impact forces were calculated by measuring the pendulum bob’s velocity and acceleration. The performance of the various STISs was measured in terms of voltage due to impact force, with forces varying from 180 to 722 N. Through data analysis, the threshold impact forces in the linear range were determined. Through an analysis of linear regression, the sensors’ sensitivity was assessed. Also, a simplified model was developed to measure the force distribution in the 2 × 2 STIS areas from the measured voltages. The results showed that improving the SPC thickness could obtain improved sensor behavior. However, for impacts that exceeded the threshold, the suggested sensor did not respond by reflecting the actual impact forces, but it gave helpful information about the impact distribution on the sensor regardless of the accurate expected linear response. Results showed that the proposed STIS performs satisfactorily within a range and has the potential to be used in the development of an e-helmet with a large STIS matrix that could cover the whole head within the e-helmet. This work also encourages future research, especially on the structure of the sensor that could withstand impacts which in turn could improve the overall range and performance and would accurately measure the impact in concussion-causing impact ranges.
Development of a Six-Degree-of-Freedom Analog 3D Tactile Probe Based on Non-Contact 2D Sensors
Authors: Albajez José Antonio , Velázquez Jesús , Torralba Marta , Díaz-Pérez Lucía C. , Yagüe-Fabra José Antonio , Aguilar Juan José
Published: May 2024

In this paper, a six-degree-of-freedom analog tactile probe with a new, simple, and robust mechanical design is presented. Its design is based on the use of one elastomeric ring that supports the stylus carrier and allows its movement inside a cubic measuring range of ±3 mm. The position of the probe tip is determined by three low-cost, noncontact, 2D PSD (position-sensitive detector) sensors, facilitating a wider application of this probe to different measuring systems compared to commercial ones. However, several software corrections, regarding the size and orientation of the three LED light beams, must be carried out when using these 2D sensors for this application due to the lack of additional focusing or collimating lenses and the very wide measuring range. The development process, simulation results, correction models, experimental tests, and calibration of this probe are presented. The results demonstrate high repeatability along the X-, Y-, and Z-axes (2.0 µm, 2.0 µm, and 2.1 µm, respectively) and overall accuracies of 6.7 µm, 7.0 µm, and 8.0 µm, respectively, which could be minimized by more complex correction models.
A Computer Vision Framework for Structural Analysis of Hand-Drawn Engineering Sketches
Authors: Joffe Isaac , Qian Yuchen , Talebi-Kalaleh Mohammad , Mei Qipei
Published: May 2024

Structural engineers are often required to draw two-dimensional engineering sketches for quick structural analysis, either by hand calculation or using analysis software. However, calculation by hand is slow and error-prone, and the manual conversion of a hand-drawn sketch into a virtual model is tedious and time-consuming. This paper presents a complete and autonomous framework for converting a hand-drawn engineering sketch into an analyzed structural model using a camera and computer vision. In this framework, a computer vision object detection stage initially extracts information about the raw features in the image of the beam diagram. Next, a computer vision number-reading model transcribes any handwritten numerals appearing in the image. Then, feature association models are applied to characterize the relationships among the detected features in order to build a comprehensive structural model. Finally, the structural model generated is analyzed using OpenSees. In the system presented, the object detection model achieves a mean average precision of 99.1%, the number-reading model achieves an accuracy of 99.0%, and the models in the feature association stage achieve accuracies ranging from 95.1% to 99.5%. Overall, the tool analyzes 45.0% of images entirely correctly and the remaining 55.0% of images partially correctly. The proposed framework holds promise for other types of structural sketches, such as trusses and frames. Moreover, it can be a valuable tool for structural engineers that is capable of improving the efficiency, safety, and sustainability of future construction projects.
Anthropomorphic Tendon-Based Hands Controlled by Agonist–Antagonist Corticospinal Neural Network
Authors: García-Córdova Francisco , Guerrero-González Antonio , Hidalgo-Castelo Fernando
Published: May 2024

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist–antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems.
Construction and Characteristics Analysis of the Xi’an Public Transport Network Considering Single-Mode and Multi-Mode Transferring
Authors: Sun Ruifen , Xie Fengjie , Huang Sirui , Shao Yang
Published: May 2024

The connectivity of the urban public transport network and the convenience of transfers between modes of transit are important factors that affect whether passengers choose public transport. Identifying the key nodes that affect network connectivity, direct access, transfers, and clarifying the main factors that restrict the network efficiency play important roles in improving the efficiency of the public transport network and establishing a “green city”. On this premise, this paper constructs two single-layer networks and a composite network that can reflect the transfer relationship between ‘bus–bus’, ‘metro–metro’, and ‘metro–bus’ based on the method of Space-P. The composite network realizes the integration study of homogeneous and heterogeneous stops, lines, and transfer relationships in the public transport network. At the same time, five kinds of centrality indexes are applied to the transport transfer network, and the significance of these indexes in the network is explained. Through the comprehensive analysis of these five types of indexes, the key nodes affecting the network connectivity, direct access and transfer efficiency, can be identified more accurately. Taking the public transport network of Xi’an as an example, the structural characteristics of the networks, including scale-free and small-world characteristics, were empirically analyzed. The main stops that play important roles in networks were identified based on the integrated centrality, degrees, and weight degrees. The research results showed the following: (1) Xi’an’s metro network, bus network, and metro–bus composite network all have scale-free and small-world characteristics. (2) The influence of the key stops of the metro network is concentrated, while the influence of the key stops of the bus network is scattered. (3) The public transport network in the first ring road area of Xi’an has the highest degree of direct access, and the core areas of the south, west, and north of Xi’an also have high direct access. However, the direct access in the area east of Xi’an is slightly lower. (4) Xi’an’s bus transport network covers a large area, showing the characteristics of a dual-core “central + southern” network. (5) The metro–bus composite network demonstrates a closer connection between stops and a more balanced network. (6) Finally, the degree of direct access to stops in the bus transport network and metro transport network shows the characteristics of “the single core is dominant, and the circle diffusion weakens step by step”.
Developing a Conceptual Framework for Characterizing and Measuring Social Resilience in Blue-Green Infrastructure (BGI)
Authors: Campbell Angie , Chanse Victoria , Schindler Mirjam
Published: May 2024

Many cities are increasingly adopting blue-green infrastructure (BGI) to bolster their resilience against environmental challenges. Beyond its well-acknowledged environmental benefits, the role of BGI in enhancing social resilience is becoming an equally important area of focus. However, the integration of BGI in fostering social resilience presents complexities, stemming from the evolving and occasionally ambiguous definition of social resilience. Considering the broad application of BGI across various disciplines makes the evaluation of social resilience within a BGI framework complex. Consequently, a structured approach to develop a clear framework tailored to understanding and measuring social resilience in a BGI setting is needed. This study consolidates various existing frameworks of social resilience, especially utilizing the detailed 5S framework proposed by Saja et al. It integrates findings from an extensive review of literature on social resilience to develop a novel conceptual framework—the BGI Social Resilience Framework. This new framework specifically aims to capture the distinct social aspects and advantages associated with BGI. The BGI Social Resilience Framework is organized into a three-tier model, focusing on four critical aspects of social resilience—social values, social capital, social structure, and social equity—and explores how these aspects are interconnected. Characteristics and indicators are customized to accommodate the context of BGI in a way that integrates the physical and human dimensions within a comprehensive approach to measurement that uses a combination of qualitative and quantitative methods. Specifically, this research formulates a theoretical framework for BGI with the aim of investigating BGI strategies and viewpoints that bolster social resilience. The BGI Social Resilience Framework takes into account the varied demographics and the physical characteristics of urban areas to explore ways to create BGI spaces that are more inclusive and that contribute to the enhancement of social resilience.
Towards High-Efficiency Buildings for Sustainable Energy Transition: Standardized Prefabricated Solutions for Roof Retrofitting
Authors: Pennacchia Elisa , Romeo Carlo , Zylka Claudia
Published: May 2024

Enhancing energy efficiency in buildings plays a pivotal role in realizing the ambitious objective of achieving carbon neutrality by 2050, as outlined in the European Green Deal. Roofs represent the technical element most affected by energy phenomena related to heat transfer: in winter, roofing can lose up to 35% of heat, and the summer heat flux can even be higher. This paper provides a catalogue of optimized and sustainable solutions, with a specific focus on standardization and prefabrication principles, for enhancing the energy efficiency of the most prevalent types of roofs that characterize the national residential building heritage. The methodological approach that guided the research presented in this article was based on the identification and study of the most common roofings in the diverse national residential building heritage, followed by their classification according to their construction era. In the context of essential energy retrofitting of deteriorated residential building stock, 21 optimized standardized solutions have been identified. The outcome of performance evaluations of the proposed solutions allowed the implementation of a matrix that can be a valuable support for designers in selecting the most efficient precalculated and prefabricated solutions for the national residential building heritage based on energy performance and sustainability criteria.
Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors
Authors: Jang Moon-Jong , Oh Eunsung
Published: May 2024

This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization.
Phytostabilization of Heavy Metals and Fungal Community Response in Manganese Slag under the Mediation of Soil Amendments and Plants
Authors: Wang Hao , Liu Hui , Su Rongkui , Chen Yonghua
Published: May 2024

The addition of soil amendments and plants in heavy metal-contaminated soil can result in a significant impact on physicochemical properties, microbial communities and heavy metal distribution, but the specific mechanisms remain to be explored. In this study, Koelreuteria paniculata was used as a test plant, spent mushroom compost (SMC) and attapulgite (ATP) were used as amendments, and manganese slag was used as a substrate. CK (100% slag), M0 (90% slag + 5% SMC + 5% ATP) and M1 (90% slag + 5% SMC + 5% ATP, planting K. paniculata) groups were assessed in a pilot-scale experiment to explore their different impacts on phytoremediation. The results indicated that adding the amendments significantly improved the pH of the manganese slag, enhancing and maintaining its fertility and water retention. Adding the amendments and planting K. paniculata (M1) significantly reduced the bioavailability and migration of heavy metals (HMs). The loss of Mn, Pb and Zn via runoff decreased by 15.7%, 8.4% and 10.2%, respectively, compared to CK. K. paniculata recruited and enriched beneficial fungi, inhibited pathogenic fungi, and a more stable fungal community was built. This significantly improved the soil quality, promoted plant growth and mitigated heavy metal toxicity. In conclusion, this study demonstrated that the addition of SMC-ATP and planting K. paniculata showed a good phytostabilization effect in the manganese slag and further revealed the response process of the fungal community in phytoremediation.
Assessing the Impact of the COVID-19 Pandemic on Pregnant Women’s Attitudes towards Childhood Vaccinations: A Cross-Sectional Study
Authors: Arcaro Paola , Nachira Lorenza , Pattavina Fabio , Campo Enrica , Mancini Rossella , Pascucci Domenico , Damiani Gianfranco , Carducci Brigida , Spadea Antonietta , Lanzone Antonio , Bruno Stefania , Laurenti Patrizia
Published: May 2024

The COVID-19 pandemic has globally disrupted immunisation practices, impacting vulnerable populations such as pregnant women (PW), who harbour concerns about future children’s immunisations. This study aimed to assess the pandemic’s impact on PW’s attitudes towards childhood vaccinations. During three consecutive flu seasons from October 2019 to January 2022, a cross-sectional study was conducted in a large Italian teaching hospital using a questionnaire. The chi-square test was performed to compare each season. Across the 2019–2020 to 2021–2022 seasons, course attendance by PW surged from 105 to 340. Significant shifts in vaccination intentions were noted, including a 7.5% decrease in measles vaccination intent (p = 0.02) and a 10% decrease in that of pertussis (p = 0.004) from 2019–2020 to 2020–2021. While perceived contagion risk decreased, disease severity perceptions increased, with few significant differences. A statistically significant reduction was noted in the proportion of participants suspecting economic motives behind NHS workers’ promotion of childhood vaccinations. Furthermore, the pandemic period saw an increase in the perceived utility of non-institutional websites and the advice of physicians outside the NHS. These findings will help develop evidence-based, tailored interventions and communication strategies to address vaccine hesitancy and ensure optimal vaccination coverage among children born during and after the pandemic.
Promising Cytokine Adjuvants for Enhancing Tuberculosis Vaccine Immunity
Authors: Cao Xuezhi , Fu Yang-Xin , Peng Hua
Published: May 2024

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), remains a formidable global health challenge, affecting a substantial portion of the world’s population. The current tuberculosis vaccine, bacille Calmette–Guérin (BCG), offers limited protection against pulmonary tuberculosis in adults, underscoring the critical need for innovative vaccination strategies. Cytokines are pivotal in modulating immune responses and have been explored as potential adjuvants to enhance vaccine efficacy. The strategic inclusion of cytokines as adjuvants in tuberculosis vaccines holds significant promise for augmenting vaccine-induced immune responses and strengthening protection against M. tuberculosis. This review delves into promising cytokines, such as Type I interferons (IFNs), Type II IFN, interleukins such as IL-2, IL-7, IL-15, IL-12, and IL-21, alongside the use of a granulocyte–macrophage colony-stimulating factor (GM-CSF) as an adjuvant, which has shown effectiveness in boosting immune responses and enhancing vaccine efficacy in tuberculosis models.
Safety, Tolerability, and Immunogenicity of aH5N1 Vaccine in Adults with and without Underlying Medical Conditions
Authors: Jelinek Tomas , Schwarz Tino F. , Reisinger Emil , Malfertheiner Peter , Versage Eve , Van Twuijver Esther , Hohenboken Matthew
Published: May 2024

Influenza pandemics pose a serious risk to the global population, with the potential for high morbidity and mortality. An adjuvanted H5N1 vaccine (aH5N1) has been approved for prophylaxis against the avian influenza virus H5N1, which is a likely cause of future pandemics. In this phase-III, stratified, randomized, controlled, observer-blind, multicenter study, we evaluated the safety and immunogenicity of aH5N1 in four separate groups of adults: adults 18–60 years of age who were healthy or had high-risk medical conditions and older adults ≥61 years of age who were healthy or had high-risk medical conditions. Subjects were randomly assigned to aH5N1 or the comparator, adjuvanted trivalent seasonal influenza vaccine (aTIV). Antibody responses to aH5N1 were increased in all four subgroups and, within each age stratum, largely consistent between healthy subjects and those with medical conditions. Injection-site pain was reported by 66–73% of younger and 36–42% of older–aH5N1 recipients, and fatigue and myalgia were reported by 22–41% of subjects across age and health subgroups. No serious adverse events or deaths were considered related to the study vaccine. In conclusion, aH5N1 increased antibody responses regardless of age or health status and demonstrated a clinically acceptable safety and tolerability profile.
Optimizing the Extraction of Sugars from Sewage Sludge Using Ultrasound Combined with Thermal–Alkali
Authors: Fan Yujie , Li Qunshuai , Fonseca Frederico Gomes , Song Jianyang , Gong Miao
Published: May 2024

The extraction and utilization of sugars from readily available and cost-effective sewage sludge increases the economic potential of this residue, contributing to sustainable urban development. The work presented here presents a novel method in which sugars can be directly extracted from sewage sludge following an ultrasound + thermal–alkali pretreatment. The best results indicated that by subjecting the sludge to a 240 W ultrasound for 20 min, followed by alkali digestion using 6 mL of a 2 M NaOH solution at 48 °C for 60 min, it was possible to maximize the yield of crude sugar (34.22 wt.% dry) with the purity of crude sugar at 46.80%, reaching an extraction efficiency of 99.84%. Response surface methodology was used to optimize the crude sugar yields based on experimental data, reaching a value of 34.67 wt.% dry when employing an ultrasound exposure time of 12.5 min and 6 mL of the NaOH solution for a digestion time of 57.5 min; these results were considered consistent with the experimental data.
Identifikasi Ujaran Kebencian Pada Sosial Media Bahasa Inggris Menggunakan Recursive Neural Network
Authors: Pardede Jasman , Alfyansyah Rangga
Published: May 2024

Penyebaran ujaran kebencian pada sosial media telah menjadi masalah serius karena menciptakan serangkaian konflik ras, etnis, orientasi seksual, kebangsaan, dan agama. Penelitian ini menggali dari unggahan ujaran kebencian di media sosial. Ujaran kebencian semakin meningkat di media sosial yang merendahkan individu atau kelompok banyak ditemukan. Mengidentifikasi ujaran kebencian secara tepat menjadi penting untuk menganalisis sentimen publik dari kelompok pengguna terhadap kelompok lain serta mencegah kegiatan yang tidak diinginkan. Dengan mengidentifikasi akar penyebabnya, pihak-pihak yang berwenang dan platform-platform digital dapat mengambil tindakan yang lebih efektif dalam memitigasi dampak negatifnya dan melindungi masyarakat dari ancaman yang mungkin timbul. Algoritma seperti Support Vector Machine dan Deep Neural Networks, terutama Recursive Neural Network, dapat mengidentifikasi ujaran kebencian. Dalam penelitian ini menerapkan Recursive Neural Network untuk mengidentifikasi ujaran kebencian. Performa sistem ini diukur berdasarkan nilai precision, recall, accuracy, dan F-Measure. Hasil eksperimen yang dilakukan terhadap data ujaran kebencian diperoleh nilai rata-rata precision, recall, accuracy, dan F-Measure masing-masing sebesar 0.78, 0.74, 0.76, dan 0.76. Hasil tersebut menunjukkan bahwa Recursive Neural Networks yang diusulkan memiliki performa yang cukup baik.
Sistem Evaluasi Pada Aplikasi Akademik Menggunakan Metode Skala Likert Dan Algoritma Naïve Bayes
Authors: Erinsyah Moh. Fiqih , Sasmito Ginanjar Wiro , Wibowo Dega Surono , Bakti Very Kurnia
Published: May 2024

Harapan Bersama Polytechnic, Tegal City (Harber Polytechnic), is one of the higher education institutions. At the Harber Polytechnic there are various fields in carrying out the listed sections, one of which is the Information and Communication Technology (ICT) field. An agency must have a system that is used to facilitate and carry out its goals. An academic system or application used by agencies, namely Oase and Syncnau. From the existence of this system, of course, a place or place is needed to listen to it, to find out how satisfied the users are who often use the system, which is realized in the form of a web repository and evaluation. The web repository is used to store the academic system data and evaluation results using two methods, namely the Likert Scale Method and the Naïve Bayes Algorithm. Of the two methods used as a choice of questionnaires and text mining from user reviews who have filled out the questionnaire. The Likert scale method used uses four Likert levels, namely strongly agree, agree, disagree and strongly disagree. whereas for text mining it was made using the Naïve Bayes algorithm from the results of the build model to get a fairly high percentage quality level of 83.6%, and the test results of the model were also quite satisfactory so that the use of text mining is useful in opening an institution's academic system. These results have been analyzed by this method and the final result of the evaluation is the percentage level of system satisfaction and the results of comments or reviews that have been classified to make it easier for developers to develop academic systems in the future.
Implementasi Face Recognition dan Geolocation Pada Sistem Presensi Karyawan Berbasis Mobile Apps
Authors: Wibowo Bagus Budi , Setiawan Eko Budi
Published: May 2024

Teknologi informasi dan komunikasi dapat digunakan pada sistem presensi karyawan. Tujuan penelitian ini mengimplementasikan teknologi face recognition dan geolocation pada sistem presensi karyawan di PT. Motiolabs Indonesia berbasis mobile Android. Perusahaan yang menjadi tempat penelitian ini memperbolehkan para karyawannya untuk melakukan presensi secara mobile, namun belum memanfaatkan teknologi geolocation dan deteksi wajah sehingga prosees otentifikasi dan verifikasi presensi masih kurang efektif. Metode penelitian ini terdiri dari beberapa tahapan yaitu analisis permasalahan, perancangan arsitektur sistem dan teknologi, penerapan teknologi, implementasi dan pengujian sistem. Teknologi geolocation yang digunakan pada penelitian ini yaitu Global Position System dan Google Maps. Face recognition yang digunakan menggunakan Google Machine Learning Kit serta Face API JS. Hasil pengujian pendeteksian wajah berhasil mendeteksi wajah dengan maksimal tiga wajah dalam satu frame kamera sewaktu pengujian. Pendeteksian wajah berhasil dilakukan dengan maksimal jarak 0,3 meter dan mendapatkan waktu paling cepat pendeteksian paling cepat selama 45 detik. Berdasarkan pengujian geolocation sudah memberikan hasil yang akurat dan berhasil mendapatkan lokasi koordinat yang sesuai dari posisi presensi dilakukan. Setelah dilakukan pengujian kepada para stakeholder yakni para Karyawan dan pihak HRD perusahaan, aplikasi hasil penelitian ini berhasil mempermudah pihak perusahaan dalam mencatat dan memverifikasi data presensi.
Pembangunan Game Edukasi Interaktif Pelajaran Bahasa Jawa Indramayu pada SDN 1 Margadadi
Authors: Priadi Revano Fabiansyah , Agustia Richi Dwi
Published: May 2024

This research aims to improve the abilities of students at Elementary School 1 Margadadi in reading and writing the Javanese language of Indramayu. The process involved conducting observations and data collection, followed by analyzing the existing issues by gathering grade data from Class V for the past 2 years in the subjects of writing and reading in Indramayu Javanese language.. It was found that many students were not achieving the Minimum Competency Criteria. To address this issue, As a solution, an educational game for learning the Indramayu Javanese language was developed. The research methodology consisted of five stages, including problem analysis, game analysis, system design, system implementation and testing, and finally, conclusions and recommendations. This educational game utilizes the Visual, Audio, and Kinesthetic (VAK) learning method within its content. Results from testing the game with 10 student respondents showed that the average time for writing Javanese script was 13.63 seconds, which was faster than manual writing that took 27.03 seconds. The longest time recorded was 16 seconds for the letter "Nyo," as only 7 respondents were able to complete this task. Furthermore, the testing of understanding the Javanese language of Indramayu revealed that the average scores exceeded the Minimum Competency Criteria, with an average score of 84.6% for the normal level in the game and 81.4% for the hard level in the game.
Comparison of K-Means, Affinity Clustering, and Mini Batch K-Means Algorithms for Market Segmentation Analysis
Authors: Purba Andrew Castello , Handhayani Teny
Published: May 2024

Market segmentation is the process of dividing a market into homogeneous groups of buyers based on certain characteristics. Market segmentation is important for businesses to understand the needs and behaviors of their customers so that they can develop more effective marketing strategies.This study compares three clustering methods, namely K-Means Clustering, Affinity Propagation Clustering, and Mini Batch K-Means, in the context of market segmentation analysis. The data used is the marketing_campaign.csv dataset consisting of 29 columns and 2240 rows. Experiments were conducted to evaluate the performance of the three methods using the silhouette metric. The results of the experiments showed that Affinity Propagation produced the highest silhouette value of 0.5861, followed by K-Means with 0.4675, and Mini Batch K-Means with 0.4659. The finding indicates that Affinity Propagation can be a good choice for market segmentation analysis on that dataset. The results of this comparison provide insights into the strengths and weaknesses of each clustering method, helping users choose the approach that is most appropriate for their analytical goals. The study contributes to the practical understanding of the application of clustering techniques in the context of marketing.
Aspect Based Sentiment Analysis of Beauty Products Using Neighbor Weighted K-Nearest Neighbor
Authors: Hakim Fauzan Lukmanul , Dewi Kania Evita
Published: May 2024

This research aims to test the performance of Neighbor Weighted K-Nearest Neighbor (NWKNN) in handling imbalanced datasets in the case of aspect-based sentiment analysis. The data used in this research are beauty product reviews from the Kaggel site. Data obtained from 2,449 reviews. Every product review before entering the classification stage, goes through preprocessing. In this research, the preprocessing stages consist of casefolding, cleaning, tokenization, normalization, stemming, convert negation, and stopword removal processes. So that the preprocessing results can be processed by a classification algorithm, each review that has been preprocessed is included in feature extraction. The feature extraction method used in this research is TF-IDF. The results of feature extraction are included in the classification process. In this research, each review went through a classification process several times. Because in this research, multilabel handling uses binary relevance techniques. Each classification uses NWKNN. Classification was carried out four times according to the aspects used in this research, namely: price, packaging, effectiveness and aroma. So each classification produces a polarity for each aspect, namely: positive, negative, or non-sentimental. The results of performance testing with Confusion Matrix showed that NWKNN's performance was higher than KNN's for each aspect, in terms of f1-score. Where the optimal e and k values for the NWKNN method are k=40 and e=2. This shows that NWKNN proves to perform better when the dataset is imbalanced compared to KNN.
Sistem Pendingin Otomatis Panel Surya Untuk Peningkatan Daya Output Berbasis Mikrokontroler
Authors: Rahajoeningroem Tri , Jatnika Ichsan
Published: May 2024

Abstrak - Panel surya akan menghasilkan energi listrik sesuai besar intensitas cahaya yang diterimanya dari pancaran cahaya matahari, suhu panel surya yang bekerja pada standar suhu normal (250C) akan bekerja secara optimal. Apabila temperatur dari panel surya tersebut terlalu panas akan menurunkan kinerja dari sel surya tersebut. Tujuan dari penelitian ini adalah merancang sebuah sistem pendingin panel surya otomatis dengan cara mengalirkan air pada permukaan belakang panel surya dengan membuat dulu pipa saluran air pada belakang panel surya yang terbuat dari alumunium. Pada pengujian ini menggunakan panel surya 50 Wp yang menggunakan pendingin dan tanpa pendingin. Didapat hasil daya rata-rata dari panel surya tanpa pendingin sebesar 22.32 Watt dan efisiensi nya sebesar 12.62%, sedangkan daya rata-rata panel surya menggunakan pendingin didapat 27.8 Watt dan efisiensinya 15.09%. jika dibandingkan panel surya tanpa pendingin, panel surya yang menggunakan pendingin efisiensi nya lebih besar didapat peningkatan efisiensi sebesar 2.47%.
Perbandingan Performa Logika Fuzzy Tipe-1 Dan Logika Fuzzy Tipe-2 Pada Sistem Pasteurisasi Susu Berbasis Mikrokontroler
Authors: Taufiqurrahman Dhiyaa Rifqi , Pohan Muhammad Aria Rajasa
Published: May 2024

Susu merupakan bahan makanan yang mudah rusak, untuk memperpanjang ketahanan susu dapat dilakukan teknik pemanasan yang disebut dengan pasteurisasi. Proses pasteurisasi tersebut bertujuan untuk membunuh bakteri patogen yang dapat membahayakan kesehatan manusia dan meminimalkan perkembangan mikroorganisme pembusuk lainnya. Penelitian ini bertujuan untuk merancang sistem pasteurisasi susu yang mampu mengatur suhu secara stabil pada proses pasteurisasi susu metode High Temperature Short Time (HTST) menggunakan logika fuzzy tipe-1 dan logika fuzzy tipe-2.  Logika fuzzy tipe-2 merupakan pengembangan lebih lanjut dari logika fuzzy tipe-1, dengan adanya tambahan dimensi dari fungsi keanggotaannya, memungkinkan sistem logika fuzzy tipe-2 dapat merepresentasikan himpunan fuzzy yang lebih fleksibel dan dapat merepresentasikan ketidakpastian lebih baik daripada logika fuzzy tipe-1. Dua pengujian dilakukan untuk membandingkan performansi kedua sistem tersebut yaitu pada kondisi tanpa adanya gangguan (noise) dan kondisi dengan adanya gangguan. Hasilnya menunjukkan bahwa pada pengujian tanpa adanya gangguan, logika fuzzy tipe-2 unggul dalam hal maximum overshoot, sedangkan logika fuzzy tipe-1 unggul dalam rise time. Namun, dalam pengujian dengan adanya gangguan, logika fuzzy tipe-2 lebih baik dalam mencapai rise time dan settling time, serta mampu menjaga suhu tetap maupun mendekati setpoint lebih lama dibandingkan dengan logika fuzzy tipe-1.
A influência das famílias nas expectativas das crianças face à transição para a escolaridade obrigatória
Authors: Marques Sónia Fernandes , Oliveira Luís Miguel Gonçalves de
Published: May 2024

A transição para a escolaridade obrigatória é um momento delicado que acarreta diversas mudanças, que podem conduzir a sentimentos de ansiedade e receio para as crianças e para as suas famílias, se não for trabalhada com intencionalidade educativa. Assim, para combater as descontinuidades é crucial investir neste processo, criando transições felizes e saudáveis. Nesta perspetiva, foi realizado o presente ensaio investigativo de cariz qualitativo, que consiste num estudo de caso acerca dos aspetos que poderão influenciar as expectativas das crianças na transição para a escolaridade obrigatória, com a intenção de identificar as expectativas e receios das crianças e das famílias relativamente ao 1.º Ciclo do Ensino Básico. Este foi elaborado no ano letivo 2021/2022 num Jardim de Infância da periferia de Leiria e teve como participantes as 10 crianças do grupo que iam transitar para a escolaridade obrigatória, os seus pais e a educadora do grupo. Este foi norteado pela questão de partida: “Que aspetos poderão influenciar as expectativas das crianças na transição da Educação Pré-Escolar para o 1.º CEB?”. Os objetivos deste estudo prendem-se com a identificação de expectativas e receios das crianças e das suas famílias relativamente à entrada na escolaridade obrigatória. Para a recolha de dados, foram aplicados inquéritos por questionário aos pais e entrevistas às crianças e à educadora. Após a análise dos dados foi possível constatar que o que é dito pelas famílias tem uma grande influência nas expectativas das crianças acerca da nova etapa.