bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards the Development of Novel Diclofenac Multicomponent Pharmaceutical Solids
Authors: Acebedo-Martínez Francisco Javier , Alarcón-Payer Carolina , Barrales-Ruiz Helena María , Niclós-Gutiérrez Juan , Domínguez-Martín Alicia , Choquesillo-Lazarte Duane
Published: July 2022

Multicomponent pharmaceutical materials offer new opportunities to address drug physicochemical issues and to obtain improved drug formulation, especially on oral administration drugs. This work reports three new multicomponent pharmaceutical crystals of the non-steroidal anti-inflammatory drug diclofenac and the nucleobases adenine, cytosine, and isocytosine. They have been synthesized by mechanochemical methods and been characterized in-depth in solid-state by powder and single crystal X-ray diffraction, as well as other techniques such as thermal analyses and infrared spectroscopy. Stability and solubility tests were also performed on these materials. This work aimed to evaluate the physicochemical properties of these solid forms, which revealed thermal stability improvement. Dissociation of the new phases was observed in water, though. This fact is consistent with the reported observed layered structures and BFDH morphology calculations.
High-Volume Hospitals’ Ovarian Cancer Care—Less Individual Approach or Better Treatment Results?
Authors: Millert-Kalińska Sonja , Pruski Dominik , Przybylski Marcin , Stawicka-Niełacna Małgorzata , Mądry Edyta , Mądry Radosław
Published: July 2022

Ovarian cancer (OC) is the eighth most common cancer worldwide and is usually diagnosed in advanced stages. The relationship between treatment in high-volume hospitals (HVHs) and survival in OC has been documented by multiple studies, which showed that superior treatment and survival outcomes are associated with surgical expertise and multidisciplinary resources. To our study, 135 first-time patients treated in the years 2019–2020 in the Department of Oncology of Poznań University of Medical Sciences were enrolled. Th analysis showed a significant dependency between being treated in a HVH from the beginning of one’s diagnosis and the scope of the first intervention. Additionally, among patients treated in our centre, a significant portion of patients underwent laparoscopy, and from one year to another the number of laparoscopies performed increased. This may indicate that more patients began to qualify for neoadjuvant treatment. Patients benefit the most from surgery in a centre with more experience in treating ovarian cancer. In the future, we will be able to expand this study by using data from patients treated before 2019 and analysing larger cohorts of patients. This might enable us to update the rates of overall survival (OS), objective response rate (ORR) and progression-free survival (PFS).
A New Deep-Water Epilithic Green Alga, Ulvella lacustris, from an Alpine Brackish Lake in Qinghai–Tibet Plateau
Authors: Yan Qiufeng , Dai Qingyu , Liu Benwen , Liu Guoxiang , Zhu Huan
Published: July 2022

Ulvella species are widely distributed in ocean and freshwater habitats and from high latitudes to the tropics. However, no species of this genus have been found in the Qinghai–Tibetan Plateau. In the present study, five rock specimens were collected from the bottom of Qinghai Lake at a depth of about 15–28 m, from which three unialgal strains were isolated. These strains were characterized by prostrate pseudoparenchyma, irregular or radial branch filaments, and mono- or polystromatic discs or rosettes formed by consecutive filaments, with thalli that can grow up to 180 um in length, suggesting that they may be Ulvella species. Phylogenetic analysis based on 18S rDNA and tufA cpDNA sequences revealed that the isolates formed a strong branch among other Ulvella species and were closely related to Ulvella leptochaete and Ulvellawaernii. This result was also confirmed by neighbor-joining tree inference based on ITS2 secondary structure. Unlike other members of Ulvella, the isolated strain was characterized by its unique habitat, field morphology of thallus, size of vegetative cells, and number of pyrenoids. Based on these morphological differences, the phylogenetic analysis, and the comparison of ITS2 secondary structure with relative species, the strain isolated in this study was proposed to be a novel Ulvella sp. The alga was found on the bottom of brackish lakes on the Qinghai–Tibetan Plateau, which meant that our sampling range needed to be expanded further.
Efficacy of Endobiliary Radiofrequency Ablation in Preserving Survival, Performance Status and Chemotherapy Eligibility of Patients with Unresectable Distal Cholangiocarcinoma: A Case-Control Study
Authors: Sandru Vasile , Ungureanu Bogdan Silviu , Stan-Ilie Madalina , Oprita Ruxandra , Balan Gheorghe G. , Plotogea Oana-Mihaela , Rinja Ecaterina , Butuc Andreea , Panaitescu Afrodita , Constantinescu Alexandru , Gheonea Dan Ionut , Constantinescu Gabriel
Published: July 2022

Background: Cholangiocarcinoma is the most common malignancy of the bile ducts causing intrahepatic, hilar, or distal bile duct obstruction. Most jaundiced patients are diagnosed with unresectable tumors in need for palliative bile duct drainage and chemotherapy. Endobiliary radiofrequency ablation (RFA) is an adjuvant technique that may be applied prior to biliary stenting. The aim of our study was to assess the efficacy of endobiliary RFA prior to stent insertion in patients with unresectable distal cholangiocarcinomas. Methods: Twenty-five patients (eight treated with RFA and stenting and 17 treated with stenting alone) were included in a case-controlled study. We prospectively assessed the impact of RFA on the survival rate, the patient performance status, and the preservation of eligibility for chemotherapy based on the patient laboratory profile. Results: Patients treated with RFA prior to stenting proved to have a significantly longer survival interval (19 vs. 16 months, p = 0.04, 95% CI) and significantly better performance status. Moreover, the laboratory profiles of patients treated with RFA has been proven superior in terms of total bilirubin, liver enzymes, and kidney function, thus making patients likely eligible for palliative chemotherapy. Post-ERCP adverse events were scarce in both the study group and the control group. Conclusion: Given the isolated adverse events and the impact on the patient survival, performance, and laboratory profile, RFA can be considered safe and efficient in the management of patients with unresectable distal cholangiocarcinomas.
Thousands of Women’s Lives Depend on the Improvement of Poland’s Cervical Cancer Screening and Prevention Education as Well as Better Networking Strategies Amongst Cervical Cancer Facilities
Authors: Śniadecki Marcin , Poniewierza Patryk , Jaworek Paulina , Szymańczyk Ada , Andersson Gorm , Stasiak Maria , Brzeziński Michał , Bońkowska Małgorzata , Krajewska Magdalena , Konarzewska Joanna , Klasa-Mazurkiewicz Dagmara , Guzik Paweł , Wydra Dariusz Grzegorz
Published: July 2022

Proper targeted cancer prophylaxis reduces the incidence of cancer in all forms; this includes cancers with significant progression potential and poor prognosis. Based on the assumption that one of the risk factors of cervical cancer is the avoidance of screening tests, we analyzed the current scenario of cervical cancer (CC) screening and recommendations in Poland (country with a well-off socioeconomic status). Based on the comprehensive literature review concerning documents of guidelines and recommendations of various bodies, including national ones, data on the implementation of CC screening in Poland, and different models for medium-to-high-income countries, we proposed how the CC screening strategy could be improved. Finally, the new strategy was further developed for those who are prone to not being screened. The proposal on how to improve the Polish CC screening program is the following: refinement of the public education on CC risk factors, popularization of CC screening incentives amongst the public, and improvement of networking strategies between CC screening facilities (“cervical screening clinical”), allowing screenings to be more efficient and rapid. We believe that, to enhance the future quality of life of those with rapid CC progression by catching the disease preemptively and limiting the sequelae of the disease, we have to improve education and access to medical services.
How Much Pressure Do Students Need to Achieve Good Grades?—The Relevance of Autonomy Support and School-Related Pressure for Vitality, Contentment with, and Performance in School
Authors: Martinek Daniela , Zumbach Joerg , Carmignola Matteo
Published: July 2022

This study investigates, based on Deci and Ryan’s self-determination theory, how autonomy support and school-related pressure are associated with students’ vitality, their contentment with and academic performance in school, and whether feeling related to teachers and feeling competent mediate these relations. In total, 812 secondary school students participated in this questionnaire-based survey. Perceived autonomy support was positively related while school-related pressure was negatively related with vitality and contentment. Relations were partially mediated by relatedness to teachers and perceived competence. In sum, this study provides insight into how autonomy support contributes not only to better academic achievement but also to students feeling vital in school and experiencing contentment with school environments. Moreover, the results emphasize that pressure is not only irrelevant for academic performance, but rather, detrimental for students’ perceptions in school. The practical implications imply that teachers should be trained to avoid unnecessary coercion and to strengthen their abilities in supporting their students’ autonomy. This contributes to make school a productive and enjoyable environment for learners and teachers alike.
Barriers to Change: Social Network Interactions Not Sufficient for Diffusion of High-Impact Practices in STEM Teaching
Authors: Reding Tracie , Moore Christopher , Pelton Julie A. , Edwards Sarah
Published: July 2022

We examined the relationship between faculty teaching networks, which can aid with the implementation of didactic high-impact practices (HIPs) in classroom instruction, and the actual implementation of said practices. Participants consisted of STEM faculty members that teach introductory courses at a USA research university. A total of 210 faculty were invited to complete the Teaching Practices Inventory (TPI), which measures the use of classroom-based HIPs, and were then directed to a follow-up survey to gather teaching network data if they qualified. A total of 90 faculty completed the TPI, with 52 respondents completing the network analysis portion. Ego-level data, as well as network structural position data, were collected through roster format listing all invited faculty. No correlations were found between these network metrics and TPI score. Furthermore, respondents with similar TPI scores showed no preference for interactions within their group. For example, faculty with widely varying TPI scores interacted with each other with no indications of HIPs diffusion. Although the literature suggests strong teaching networks are a necessary condition for broad diffusion of HIPs, these results indicate that such networks are not a sufficient condition. This has implications for the diffusion of HIPs specifically and institutional change generally. Engaging individuals that possess both structural positions and pedagogical knowledge may be needed to help strategically diffuse HIPs in their own networks, with institutional support and guidance most likely also required.
Relations between the Home Learning Environment and the Literacy and Mathematics Skills of Eight-Year-Old Canadian Children
Authors: Skwarchuk Sheri-Lynn , Douglas Heather , Cahoon Abbie , LeFevre Jo-Anne , Xu Chang , Roy Emilie , Simms Victoria , Wylie Judith , Maloney Erin A. , Osana Helena P. , Susperreguy María Inés
Published: July 2022

The home learning environment includes parental activities, attitudes, affect, knowledge, and resources devoted to supporting children’s development, including literacy and mathematics skills. These factors are related to the academic performance of preschool children (aged 3 to 6 years), before formal schooling and possibly beyond. In the present research, we examined the home learning environment of Canadian families as reported by either the mother (n = 51) or father (n = 30) of their Grade 3 child (n = 81; Mage = 8.7 years; range 8 to 9 years of age). Importantly, mothers’ and fathers’ reports of the home learning environment for school children were similar. For literacy, parents’ knowledge of children’s books and attitudes toward literacy were related to children’s vocabulary skills; home literacy was not related to word reading skills. For mathematics, parents’ reports of the frequency of activities such as practicing arithmetic facts and their attitudes toward mathematics were related to children’s arithmetic fluency. Other aspects of the home learning environment (time spent helping with homework, parents’ math anxiety) were not related to children’s performance. These results suggest some continuity between home learning environments and academic skills after children’s transition to school.
Technology-Immune/Technology-Enabled Problem Solving as Agency of Design-Based Mathematics Education
Authors: Abramovich Sergei
Published: July 2022

The paper is written as a reflection on the author’s online teaching of a graduate elementary mathematics content and methods course through the asynchronous modality. The need for developing skills in posing problems that integrate non-technological and technological problem-solving strategies is emphasized. Such problems are considered agency of design-based mathematics education. The importance of revealing hidden mathematical ideas stemming from teacher candidates’ willingness to explore self-posed problems within the forum components of the course is discussed. Using these hidden ideas, the paper demonstrates connectivity among different mathematical concepts including historical connections. Teacher candidates’ reflections on the course have been included and reviewed.
AI-Driven Performance Modeling for AI Inference Workloads
Authors: Sponner Max , Waschneck Bernd , Kumar Akash
Published: July 2022

Deep Learning (DL) is moving towards deploying workloads not only in cloud datacenters, but also to the local devices. Although these are mostly limited to inference tasks, it still widens the range of possible target architectures significantly. Additionally, these new targets usually come with drastically reduced computation performance and memory sizes compared to the traditionally used architectures—and put the key optimization focus on the efficiency as they often depend on batteries. To help developers quickly estimate the performance of a neural network during its design phase, performance models could be used. However, these models are expensive to implement as they require in-depth knowledge about the hardware architecture and the used algorithms. Although AI-based solutions exist, these either require large datasets that are difficult to collect on the low-performance targets and/or limited to a small number of target platforms and metrics. Our solution exploits the block-based structure of neural networks, as well as the high similarity in the typically used layer configurations across neural networks, enabling the training of accurate models on significantly smaller datasets. In addition, our solution is not limited to a specific architecture or metric. We showcase the feasibility of the solution on a set of seven devices from four different hardware architectures, and with up to three performance metrics per target—including the power consumption and memory footprint. Our tests have shown that the solution achieved an error of less than 1 ms (2.6%) in latency, 0.12 J (4%) in energy consumption and 11 MiB (1.5%) in memory allocation for the whole network inference prediction, while being up to five orders of magnitude faster than a benchmark.
Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data
Authors: Nagy Benedek , Gabor Manuela Rozalia , Bacoș Ioan Bogdan
Published: July 2022

Our exploratory research focuses on the possible relations between tourism and the mobility of people, using short longitudinal data for mobility dimensions during the COVID-19 pandemic. One of these is real-time, exhaustive type data, published by Google, about the mobility of people in six different dimensions, (retail, parks, residential, workplace, grocery, and transit). The aim is to analyze the directional, intensity, causal, and complex interplay between the statistical data of tourism and mobility data for Romanian counties. The main objective is to determine if real-world big data can be linked with tourism arrivals in the first 14 months of the pandemic. We have found, using correlations, factorial analysis (PCA), regression models, and SEM, that there are strong and/or medium relationships between retail and parks and overnights, and weak or no relations between other mobility dimensions (workplace, transit). By applying factorial analysis (PCA), we have regrouped the six Google Mobility dimensions into two new factors that are good predictors for Romanian tourism at the county location. These findings can help provide a better understanding of the relationship between the real movement of people in different urban areas and the tourism phenomenon: the GM parks dimension best predicts tourism indicators (overnights), the GM residential dimension correlates inversely with the tourism indicator, and the rest of the GM indices are generally weak predictors for tourism. A more complex analysis could signal the potential and the character of tourism in different destinations, by territorially and chronologically determining the GM indices that are better linked with the tourism statistical indicators. Further research is required to establish forecasting models using Google Mobility data.
A New Modified MARS Cryptosystem Based on Niho Exponent with an Enhanced S-Box Generation
Authors: Ahmed Fatma , Elkamchouchi Dalia H.
Published: July 2022

As an essential cryptological element, symmetric-key block ciphers have long been utilized to offer information security. Even though they were created to provide data confidentiality, their adaptability grants them to be utilized in the creation of various cryptological techniques, including message authentication protocols, hash functions, and stream cryptograms. MARS is a symmetric shared-key block cryptosystem that supports 128-bit blocks and keys with sizes ranging from 128 to 448 bits. The cryptographic cores of MARS come in a variety of rounds, each constructed to take benefit of the robust outcomes in order to enhance security and performance over earlier ciphers. The MARS cipher is given a new function in this work that uses the operations ROT, XOR, NOP, INV, hash 512, Quotient, and MOD for improving the technique of the cipher. The goal of our modification is attaining a superior confusion level whilst retaining the MARS cryptosystem’s differential and linearity aspects.
Distributed Radar Target Detection Based on RF-SSA in Non-Gaussian Noise
Authors: Chang Jiayun , Fu Xiongjun , Zhao Congxia , Lang Ping , Feng Cheng
Published: July 2022

Distributed radar target detection in non-Gaussian noise, modeled as the sum of K-distributed clutter plus thermal noise, is considered in this paper. The conventional target techniques, e.g., constant false-alarm rate (CFAR), scatterer density-dependent generalized likelihood ratio test (SDD-GLRT), and energy integration (EI) detectors, have limited performance. On the other hand, since radar target detection can be considered a classification task, deep learning techniques have been widely applied as radar detectors in recent years, but such techniques require a larger amount of training samples to prevent overfitting, which is time-consuming. To balance detection efficiency and accuracy, this paper proposes an improved random forest algorithm based on the sparrow search algorithm (RF-SSA). First, we propose a mixed method of 3DT space-time adaptive processing and wavelet denoising (3DT-WD) to improve the output signal-to-clutter plus-noise ratio. Then, the SSA is applied to the RF algorithm to adaptively obtain the optimal parameters of the detection model. The simulation results show that the proposed RF-SSA ensures higher detection performance than the other classical methods, showing a gain of about 2 dB at the same detection probability. Moreover, the detection results of the real data further confirm the superiority of the proposed RF-SSA.
Improved Image Fusion Method Based on Sparse Decomposition
Authors: Qin Xiaomei , Ban Yuxi , Wu Peng , Yang Bo , Liu Shan , Yin Lirong , Liu Mingzhe , Zheng Wenfeng
Published: July 2022

In the principle of lens imaging, when we project a three-dimensional object onto a photosensitive element through a convex lens, the point intersecting the focal plane can show a clear image of the photosensitive element, and the object point far away from the focal plane presents a fuzzy image point. The imaging position is considered to be clear within the limited size of the front and back of the focal plane. Otherwise, the image is considered to be fuzzy. In microscopic scenes, an electron microscope is usually used as the shooting equipment, which can basically eliminate the factors of defocus between the lens and the object. Most of the blur is caused by the shallow depth of field of the microscope, which makes the image defocused. Based on this, this paper analyzes the causes of defocusing in a video microscope and finds out that the shallow depth of field is the main reason, so we choose the corresponding deblurring method: the multi-focus image fusion method. We proposed a new multi-focus image fusion method based on sparse representation (DWT-SR). The operation burden is reduced by decomposing multiple frequency bands, and multi-channel operation is carried out by GPU parallel operation. The running time of the algorithm is further reduced. The results indicate that the DWT-SR algorithm introduced in this paper is higher in contrast and has much more details. It also solves the problem that dictionary training sparse approximation takes a long time.
Breast Cancer Histopathological Image Recognition Based on Pyramid Gray Level Co-Occurrence Matrix and Incremental Broad Learning
Authors: Li Jia , Shi Jingwen , Su Hexing , Gao Le
Published: July 2022

In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model. The PGLCM model is designed to extract the fusion features of breast cancer histopathological images, which can reflect the multiresolution useful information of the images and facilitate the improvement of the classification effect in the later stage. The IBL model is used to improve the classification accuracy by increasing the number of network enhancement nodes horizontally. Unlike deep neural networks, the IBL model compresses the training and testing time cost greatly by making full use of its single-hidden-layer structure. To our knowledge, it is the first attempt for the IBL model to be introduced into the breast cancer histopathological image recognition task. The experimental results in four magnifications of the BreaKHis dataset show that the accuracy of binary classification and eight-class classification outperforms the existing algorithms. The accuracy of binary classification reaches 91.45%, 90.17%, 90.90% and 90.73%, indicating the effectiveness of the established combined model and demonstrating the advantages in breast cancer histopathological image recognition.
AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks
Authors: Hanif Maria , Ashraf Humaira , Jalil Zakia , Jhanjhi Noor Zaman , Humayun Mamoona , Saeed Saqib , Almuhaideb Abdullah M.
Published: July 2022

The popularity of wireless sensor networks for establishing different communication systems is increasing daily. A wireless network consists of sensors prone to various security threats. These sensor nodes make a wireless network vulnerable to denial-of-service attacks. One of them is a wormhole attack that uses a low latency link between two malicious sensor nodes and affects the routing paths of the entire network. This attack is brutal as it is resistant to many cryptographic schemes and hard to observe within the network. This paper provides a comprehensive review of the literature on the subject of the detection and mitigation of wormhole attacks in wireless sensor networks. The existing surveys are also explored to find gaps in the literature. Several existing schemes based on different methods are also evaluated critically in terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end delay. As artificial intelligence and machine learning have massive potential for the efficient management of sensor networks, this paper provides AI- and ML-based schemes as optimal solutions for the identified state-of-the-art problems in wormhole attack detection. As per the author’s knowledge, this is the first in-depth review of AI- and ML-based techniques in wireless sensor networks for wormhole attack detection. Finally, our paper explored the open research challenges for detecting and mitigating wormhole attacks in wireless networks.
Energy Management Strategy of Fuel-Cell Backup Power Supply Systems Based on Whale Optimization Fuzzy Control
Authors: Cheng Yu , Zhang Yuan , Chen Qihong
Published: July 2022

A backup power supply system can provide electrical energy for load in harsh environment without power grid, and plays a very important role in natural disaster rescue and military application. Compared with traditional energy sources, the proton-exchange membrane fuel cell has the advantages of good low temperature start-up performance, low noise, high efficiency and high power density, and no carbon emission, which gradually becomes the main energy source of the backup power supply system. This paper designs the backup power system topology of the “dual fuel cell and lithium battery” and researches energy management strategy based on fuzzy control (FC). Considering that it is difficult for the fuzzy controller to obtain the optimal membership function parameters according to working conditions, this paper aims to reduce the hydrogen consumption of the system, uses the whale optimization algorithm to optimize the membership function of the fuzzy controller, and proposes an energy management strategy based on whale optimization fuzzy control (WO-FC). The energy management strategies are verified and compared by simulations. The results show that the membership function of the optimized fuzzy controller based on WO-FC energy management strategy has changed greatly: The hydrogen consumption of the system obviously decreased compared with no optimization, and the overall efficiency of the fuel cell also significantly improved. To be more precise, the energy management strategy based on WO-FC reduces the hydrogen consumption of the system by 5.35% and improves the overall efficiency of the fuel cell by 1.56%.
Piling Secondary Subtropical Forest Residue: Long-Term Impacts on Soil, Trees, and Weeds
Authors: Sclifó Zucon Ana Rosária , Pedreira Guilherme Quaresma , Vargas Motta Antônio Carlos , Gotz Lenir Fátima , Maeda Shizuo , Martins Bassaco Marcos Vinícius , Magri Ederlan , Prior Stephen Arthur , de Paula Souza Luiz Claudio , de Oliveira Junior Jairo Calderari
Published: July 2022

The objective of this study was to evaluate the long-term effects of piling secondary forest residue (after 3 decades) on soil chemical properties, growth, and nutrition of Pinus taeda and weeds at three locations. After secondary forest removal and residue piling, areas were cultivated with P. taeda (22 years), followed by eucalyptus (7 years), and returned to P. taeda. At 2 years of age, tree height and needle-nutrient levels of ongoing P. taeda from areas influenced by residue piling and areas outside the piling zone were evaluated. Biomass and nutrient levels of herbaceous and shrub weeds, NDVI indices (via a drone), and soil chemistry were also evaluated. Residue-piled areas displayed a decrease in soil pH and an increase in available soil P and K. Although herbaceous and shrub-weed biomass increased 2.5 to 10 times in residue-piling areas, this did not compromise pine growth. While residue piling had little impact on the nutritional status of pine and weeds, NDVI values indicated greater plant growth in piling areas. In general, the long-term effect of residue piling was an important factor associated with the large variation in tree growth and weed incidence after 3 decades.
Reduced Rainfall Variability Reduces Growth of Nothofagus alessandrii Espinosa (Nothofagaceae) in the Maule Region, Chile
Authors: Santelices-Moya Rómulo , Gibson-Carpintero Stephanie , Cabrera-Ariza Antonio , Santini-Junior Luiz , Venegas-González Alejandro
Published: July 2022

Nothofagus alessandrii Espinosa is an endemic species of the coastal Maulino forest of central Chile that has historically been severely threatened by the reduction of its habitat and the isolation of its fragments. In addition, a gradual reduction in precipitation has been observed in recent years across its entire natural distribution area. Although the genus Nothofagus has been extensively analyzed in dendrochronological studies in the Southern Hemisphere, the dendrochronological potential of this species is unknown. In this study, we developed a novel tree-ring chronology of N. alessandrii in order to examine the climate sensitivity of the radial growth and to thus understand its response to climate change in central Chile. Our ring-width chronology showed a series intercorrelation value of 0.48 for the period of 1942–2016 (EPS < 0.85, with 10 trees), showing a strong common growth signal among the trees. N. alessandrii growth was strongly influenced by precipitation from May to November (the austral winter and spring seasons), while the temperature signal was weak. We observed that the radial growth patterns of N. alessandrii chronology showed upward growth trends, with a marked positive slope until the mid-1980s. However, a negative trend was observed for the period of 1985–2016, which was related to the increased drought conditions (rainfall and soil moisture reductions) in past decades and affected the entire natural distribution of the species. We suggest that drier winters and springs would slow the growth of this species. This information is of vital importance to understanding the growth dynamics of N. alessandrii, a critically endangered species, and to take on urgent adaptation and mitigation measures in the face of climate change.
Geostatistical Analysis of Mangrove Ecosystem Health: Mapping and Modelling of Sampling Uncertainty Using Kriging
Authors: Parman Rhyma Purnamasayangsukasih , Kamarudin Norizah , Ibrahim Faridah Hanum , Nuruddin Ahmad Ainuddin , Omar Hamdan , Abdul Wahab Zulfa
Published: July 2022

This study assessed the health of the mangrove ecosystem and mapped the spatial variation in selected variables sampled across the Matang Mangrove Forest Reserve (MMFR) by using a geostatistical technique. A total of 556 samples were collected from 56 sampling points representing mangrove biotic and abiotic variables. All variables were used to generate the semivariogram model. The predicted variables over the entire MMFR have an overall prediction accuracy of 85.16% (AGB), 90.78% (crab abundance), 97.3% (soil C), 99.91% (soil N), 89.23% (number of phytoplankton species), 95.62% (number of diatom species), 99.36% (DO), and 87.33% (turbidity). Via linear weight combination, the prediction map shows that mangrove ecosystem health in Kuala Trong throughout the Sungai Kerang is excellent (5: MQI > 1.5). Some landward areas of Kuala Trong were predicted to have moderate health (3: −0.5 ≤ MQI ≤ 0.5), while Kuala Sepetang was predicted to have the bad ecosystem health (2: −1.5 ≤ MQI ≤ −0.5), with active timber harvesting operations and anthropogenic activities in the landward areas. The results of this method can be utilised to carry out the preferred restoration, through appropriate management and facilities distribution, for improving the ecosystem health of mangroves.
An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
Authors: Ahamed Farhad , Farid Farnaz , Suleiman Basem , Jan Zohaib , Wahsheh Luay A. , Shahrestani Seyed
Published: July 2022

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.
Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques
Authors: Rubart Jessica , Grimm Valentin , Potthast Jonas
Published: July 2022

The German manufacturing industry has been carrying out new developments towards the next industrial revolution, focusing on smart manufacturing environments. Our work emphasizes human-centered control rooms in the context of production plants. Increased automation does not have to come with less human control. Therefore, we report on multimodal collaborative interaction techniques to augment industrial control rooms. In particular, we include mobile workers who use the control room while being in the production hall using tablets or specifically mixed reality glasses. Collaborative annotation dashboards support discussions and a shared understanding among analysts. Manufacturing-related data can be integrated into business analytics environments so that holistic analyses can be performed. Multimodal interaction techniques can support effective interaction with the control room based on the users’ preferences. Immersive experience through mixed reality-based three-dimensional visualizations and interaction possibilities support users in obtaining a clear understanding of the underlying data.
Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
Authors: Dai Zhiqiang , Xu Gaochao , Liu Ziqi , Ge Jiaqi , Wang Wei
Published: July 2022

Unmanned aerial vehicles (UAVs) have the characteristics of portability, safety, and strong adaptability. In the case of a maritime disaster, they can be used for personnel search and rescue, real-time monitoring, and disaster assessment. However, the power, computing power, and other resources of UAVs are often limited. Therefore, this paper combines a UAV and mobile edge computing (MEC), and designs a deep reinforcement learning-based online task offloading (DOTO) algorithm. The algorithm can obtain an online offloading strategy that maximizes the residual energy of the UAV by jointly optimizing the UAV’s time and communication resources. The DOTO algorithm adopts time division multiple access (TDMA) to offload and schedule the UAV computing task, integrates wireless power transfer (WPT) to supply power to the UAV, calculates the residual energy corresponding to the offloading action through the convex optimization method, and uses an adaptive K method to reduce the computational complexity of the algorithm. The simulation results show that the DOTO algorithm proposed in this paper for the energy-saving goal of maximizing the residual energy of UAVs in MEC can provide the UAV with an online task offloading strategy that is superior to other traditional benchmark schemes. In particular, when an individual UAV exits the system due to insufficient power or failure, or a new UAV is connected to the system, it can perform timely and automatic adjustment without manual participation, and has good stability and adaptability.
Research on Urban Traffic Incident Detection Based on Vehicle Cameras
Authors: Xia Zhuofei , Gong Jiayuan , Yu Hailong , Ren Wenbo , Wang Jingnan
Published: July 2022

Situational detection in the traffic system is of great significance to traffic management and even urban management. Traditional detection methods are generally based on roadside equipment monitoring roads, and it is difficult to support large-scale and fine-grained traffic incident detection. In this study, we propose a detection method applied to the mobile edge, which detects traffic incidents based on the video captured by vehicle cameras, so as to overcome the limitations of roadside terminal perception. For swarm intelligence detection, we propose an improved YOLOv5s object detection network, adding an atrous pyramid pooling layer to the network and introducing a fusion attention mechanism to improve the model accuracy. Compared with the raw YOLOv5s, the mAP metrics of our improved model are increased by 3.3% to 84.2%, enabling it to detect vehicles, pedestrians, traffic accidents, and fire traffic incidents on the road with high precision in real time. This provides information for city managers to help them grasp the abnormal operation status of roads and cities in a timely and effective manner.
Statistical Modeling of Compressive Strength of Hybrid Fiber-Reinforced Concrete—HFRC
Authors: Quinino Uziel Cavalcanti de Medeiros , Christ Roberto , Tutikian Bernardo Fonseca , Silva Luis Carlos Pinto da
Published: July 2022

The incorporation of reinforcements is a necessity to compensate for the deficiency that concrete presents with its fragile behavior and low deformation capacity. One of the solutions to improve tensile performance is the addition of fiber in random distributions throughout the volume. However, this strategy can compromise the compressive strength of concrete; consequently, the purpose of this study was to analyze the compressive strength of conventional concrete with hybrid fiber reinforcement. A behavioral equation of compressive strength as a function of the hybridization of three types of fibers (steel, polypropylene, and carbon) was determined. This equation accounted for the proportions, as well as the binary and tertiary combinations, of fibers. Results showed that the effective participation of metallic fibers and their combination with synthetic fibers contributed positively to the performance of fiber-reinforced concrete. The gain in axial compression strength reached values in the range of 10% to 19% depending on the content of total fibers and their combination, without problems in the production process.
Study of Consumer Liking of Six Chinese Vinegar Products and the Correlation between These Likings and the Volatile Profile
Authors: Liang Shan , Liu Ying , Yuan Shao , Liu Yixuan , Zhu Baoqing , Zhang Min
Published: July 2022

As the aroma of Chinese vinegar is a key quality trait that influences consumer liking, a combination of sensory data and instrumental measurements were performed to help understand the aroma differences of six types of Chinese vinegar. A total of 52 volatile compounds, mostly ethyl acetate, acetic acid, and phenethyl alcohol, were detected in six types of Chinese vinegar using solid-phase microextraction coupled with gas chromatography–mass spectrometry (SPME-GC–MS). Combined with open-ended questions, the correlation between consumer liking and the volatile profile of the vinegar was further investigated. More consumers preferred the potato vinegar (B6) described as “having a sweet aroma and fruity vinegar aroma”. The Heng-shun Jinyou balsamic vinegar (B5) was not favored by consumers with its exhibition of “too pungent vinegar aroma”. Based on their preference patterns, consumers were grouped into three clusters by k-means clustering and principal component analysis (PCA). Using partial least squares regression (PLSR), the most important volatile compounds that drove consumer liking in the three clusters were obtained, among which 14 compounds such as 1-methylpyrrole-2-carboxaldehyde, ethyl acetate, and acetylfuran had the greatest impact on consumer liking, which could guide manufacturers to improve product quality and customer satisfaction.
Genome-Wide Association Analysis for Chronic Superficial Keratitis in the Australian Racing Greyhound
Authors: Karamatic Steven , Goode Rebecca , Bageswaran Niruba , Willet Cali E. , Samaha Georgina , Ferguson Ray , Mazrier Hamutal , Wade Claire M.
Published: July 2022

Chronic superficial keratitis (CSK) is a progressive inflammatory condition of the eye (cornea) that can cause discomfort and blindness. Differential disease risk across dog breeds strongly suggests that CSK has a genetic basis. In addition to genetic risk, the occurrence of CSK is exacerbated by exposure to ultraviolet light. Genome-wide association analysis considered 109 greyhounds, 70 with CSK and the remainder with normal phenotype at an age over four years. Three co-located variants on CFA18 near the 5′ region of the Epidermal Growth Factor Receptor (EGFR) gene were associated with genome-wide significance after multiple-test correction (BICF2P579527, CFA18: 6,068,508, praw = 1.77 × 10−7, pgenome = 0.017; BICF2P1310662, CFA18: 6,077,388, praw = 4.09 × 10−7, pgenome = 0.040; BICF2P160719, CFA18: 6,087,347, praw = 4.09 × 10−7, pgenome = 0.040) (canFam4)). Of the top 10 associated markers, eight were co-located with the significantly associated markers on CFA18. The associated haplotype on CFA18 is protective for the CSK condition. EGFR is known to play a role in corneal healing, where it initiates differentiation and proliferation of epithelial cells that in turn signal the involvement of stromal keratocytes to commence apoptosis. Further validation of the putative functional variants is required prior to their use in genetic testing for breeding programs.
A Comparison of Bioinformatics Pipelines for Enrichment Illumina Next Generation Sequencing Systems in Detecting SARS-CoV-2 Virus Strains
Authors: Afiahayati  , Bernard Stefanus , Gunadi  , Wibawa Hendra , Hakim Mohamad Saifudin , Marcellus  , Parikesit Arli Aditya , Dewa Chandra Kusuma , Sakakibara Yasubumi
Published: July 2022

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a newly emerging virus well known as the major cause of the worldwide pandemic due to Coronavirus Disease 2019 (COVID-19). Major breakthroughs in the Next Generation Sequencing (NGS) field were elucidated following the first release of a full-length SARS-CoV-2 genome on the 10 January 2020, with the hope of turning the table against the worsening pandemic situation. Previous studies in respiratory virus characterization require mapping of raw sequences to the human genome in the downstream bioinformatics pipeline as part of metagenomic principles. Illumina, as the major player in the NGS arena, took action by releasing guidelines for improved enrichment kits called the Respiratory Virus Oligo Panel (RVOP) based on a hybridization capture method capable of capturing targeted respiratory viruses, including SARS-CoV-2; therefore, allowing a direct map of raw sequences data to SARS-CoV-2 genome in downstream bioinformatics pipeline. Consequently, two bioinformatics pipelines emerged with no previous studies benchmarking the pipelines. This study focuses on gaining insight and understanding of target enrichment workflow by Illumina through the utilization of different bioinformatics pipelines named as ‘Fast Pipeline’ and ‘Normal Pipeline’ to SARS-CoV-2 strains isolated from Yogyakarta and Central Java, Indonesia. Overall, both pipelines work well in the characterization of SARS-CoV-2 samples, including in the identification of major studied nucleotide substitutions and amino acid mutations. A higher number of reads mapped to the SARS-CoV-2 genome in Fast Pipeline and merely were discovered as a contributing factor in a higher number of coverage depth and identified variations (SNPs, insertion, and deletion). Fast Pipeline ultimately works well in a situation where time is a critical factor. On the other hand, Normal Pipeline would require a longer time as it mapped reads to the human genome. Certain limitations were identified in terms of pipeline algorithm, whereas it is highly recommended in future studies to design a pipeline in an integrated framework, for instance, by using NextFlow, a workflow framework to combine all scripts into one fully integrated pipeline.
Genetic Profile of Left Ventricular Noncompaction Cardiomyopathy in Children—A Single Reference Center Experience
Authors: Piekutowska-Abramczuk Dorota , Paszkowska Agata , Ciara Elżbieta , Frączak Kamila , Mirecka-Rola Alicja , Wicher Dorota , Pollak Agnieszka , Rutkowska Karolina , Sarnecki Jędrzej , Ziółkowska Lidia
Published: July 2022

Background: Left ventricular noncompaction cardiomyopathy (LVNC) is a rare cardiac disorder characterised by the presence of a two-layer myocardium with prominent ventricular trabeculation, intertrabecular deep depressions and an increased risk of heart failure, atrial and ventricular arrhythmias and systemic thromboembolic events in affected patients. The heterogeneous molecular aetiology solved in 10%–50% of patients more frequently involves sarcomeric, cytoskeletal or ion channel protein dysfunction—mainly related to causative MYH7, TTN or MYBPC3 variants. The aim of the study was to determine the molecular spectrum of isolated LVNC in a group of children examined in a single paediatric reference centre. Methods: Thirty-one paediatric patients prospectively diagnosed with LVNC by echocardiography and cardiovascular magnetic resonance examination were recruited into the study group. The molecular analysis included next-generation sequencing (gene panel or whole exome) and classic Sanger sequencing. All selected variants with high priority were co-segregated in the available parents. Results: We identified 16 distinct variants in 11 genes in 16 patients (52%), including 10 novel alterations. The most frequent defects in our cohort were found in the genes HCN4 (n = 4), MYH7 (n = 2) and PRDM16 (n = 2). Other likely disease-causing variants were detected in ACTC1, ACTN2, HCCS, LAMA4, MYH6, RBM20, TAFFAZIN and TTN. Patients with established molecular defects more often presented with arrhythmia, thromboembolic events and death, whereas the predominant symptoms in patients with no identified molecular defects were heart failure and the presence of late gadolinium enhancement. Conclusion: This study expands the genetic and clinical spectrum of childhood LVNC. Although the molecular aetiology of LVNC varies widely, the comprehensive testing of a wide panel of cardiomyopathy-related genes helped to identify underlying molecular defects in more than half of the children in the study group. The molecular spectrum in our cohort correlated with the occurrence of arrhythmia, death and a family history of cardiomyopathy. We confirmed that genetic testing is an integral part of the work-up and management LVNC in children.