bluuebunny/crossref_metadata_2025 huggingface dataset:
Recreating the future—Indigenous research paradigms in health professional education research
Authors: McKivett Andrea , Paul David
Published: June 2023

Introduction

Health and self‐determination are recognised as universal human rights. Health professional education research and practice hold the capacity to prioritise values, worldviews and agendas that envisage sustainable and equitable futures for the entire community served. This paper explores the need for the co‐location of Indigenous research paradigms in health professional education research and teaching. Indigenous communities have a long history of science, research and sustainable living and are holders of ways of knowing, being and doing that can shape actions and priorities in health research that value equity and sustainability.

Discussion

Knowledge construction in health professional education research does not occur in isolation nor is it value neutral. A continued dominance of the biomedical approach to health creates a system of innovation that is unbalanced and unable to deliver health outcomes demanded by contemporary society. As power and hierarchies are embedded in health professional education research and praxis, transformative action is required to bring forth marginalised voices in research processes. Critical reflexivity regarding the ontological, epistemological, axiological and methodological positioning of researchers is an important step towards creating and sustaining research structures that effectively value and co‐locate different perspectives in knowledge production and translation.

Conclusion

Working towards more equitable and sustainable futures for Indigenous and non‐Indigenous communities requires health care systems to be informed and guided by different knowledge paradigms. This can work to avoid the ongoing reproduction of inefficient biomedical structures and purposefully disrupt the status quo of health inequities. Realising this requires the effective co‐location of Indigenous research paradigms and ways of working into health professional education research that centre relationality, wholism, interconnectedness and self‐determination. This calls for a raising of the critical consciousness of health professional education research academies.
Back to the ‘roots’ of research: a hypothesis‐driven approach provides predictive understanding of plant–herbivore–microbe interactions
Authors: Domeignoz‐Horta Luiz Alberto , Schuman Meredith C.
Published: June 2023

This article is a Commentary on Böttner et al. (2023), 239: 1475–1489.
Activity of multipurpose contact lens solutions against Staphylococcus aureus, Pseudomonas aeruginosa, Serratia marcescens and Candida albicans biofilms
Authors: Mendonca Jamile Reimann , Dantas Leticia Ramos , Tuon Felipe Francisco
Published: June 2023

Introduction

The use of contact lenses has progressively increased around the world, thereby increasing the risk of complications. The most serious complication is microbial keratitis (corneal infection) that can progress to a corneal ulcer.

Methods

Fourteen multipurpose contact lens solutions were tested on mature biofilms comprising Staphylococcus aureus, Pseudomonas aeruginosa, Serratia marcescens and Candida albicans, using the minimum disinfection times recommended by the manufacturers. The biofilm was induced in the lens case, and 24 h later, the solutions were added. Activity against planktonic and sessile cells was evaluated and quantified as colony forming units per millilitre. The minimum concentration for biofilm eradication was defined as a 99.9% reduction in viable cells.

Results

Although most solutions exhibited activity against planktonic cells, only five of the 14 solutions produced a significant reduction in the S. marcescens biofilm. No solution achieved the minimal biofilm eradication of S. aureus, P. aeruginosa and C. albicans.

Conclusion

Multipurpose contact lens solutions provide greater bactericidal and/or fungicidal activity on planktonic cells than biofilms. The minimal eradication biofilm concentration was only achieved for S. marcescens.
Retrospective chart review of patient socioeconomic status and language preference associated with live video telehealth in a pediatric dermatology practice
Authors: Zacher Natasha C. , Pickett Kaci L. , Schmiege Sarah J. , Olson Christina A. , Bruckner Anna L. , Kohn Lucinda L.
Published: June 2023

Pediatric teledermatology rapidly expanded with the COVID‐19 pandemic, and the impacts of this expansion on patients' access to care have not yet been entirely defined. In this retrospective study of 3027 patients in an academic pediatric dermatology practice, patients who identified as having a primary language other than English were less likely to access pediatric dermatology care during the COVID lockdown. This study did not identify a significant or meaningful difference in age, geography, socioeconomic status, ethnicity, or race between patients who were offered pediatric dermatology care that was either in‐person or via synchronous telehealth. These findings are overall reassuring that there were not major disparities in telehealth utilization during the COVID shelter‐in‐place mandate, although highlight the need for institutions to ensure systems are in place to enhance telehealth access for patients with non‐English primary language.
Ulcerated diaper dermatitis: Child abuse mimicker
Authors: Caldas Stella A. , Murphy Emily , Habeshian Kaiane , Kirkorian Anna Yasmine , Hinds Tanya
Published: June 2023

Irritant diaper dermatitis is a common dermatologic problem among infants and young children. Severe erosive presentations, although uncommon, are diagnostically challenging and can mimic non‐accidental trauma (NAT). Diagnosing inflicted injury and NAT where it does not exist can cause parental distress, yet failing to diagnose inflicted injury and NAT can result in re‐injury. We describe three cases of severe erosive diaper dermatitis in pediatric patients aged 2–6 years that were initially concerning for inflicted scald burn or neglect.
The novel HLA‐B*44:369 allele characterised by two different sequencing‐based typing techniques
Authors: Dhuyser Adèle , Silva Rodriguez Maël , Morel Thomas , Pérès Michaël , Aarnink Alice
Published: June 2023

The novel allele HLA‐B*44:369 differs from HLA‐B*44:02:01:01 by one non‐synonymous nucleotide substitution in exon 3.
Analysis of spatial distribution characteristics of electric vehicle sales and service shops and the corresponding influencing factors
Authors: Huang Wenwei , Wu Liangfu , Zeng Xuebin , Sun Ying
Published: June 2023

The electric vehicle industry is essential for China's economic transformation and industrial upgrading. Identifying the distribution of electric vehicle sales and service shops is of great significance to boost the development of retail geography theory and promote the optimization of industrial space. Regression and spatial econometric methods analyze the influencing factors of electric vehicle sales and service shop distribution. According to the point of interest data of electric vehicle sales and service shops, taking 10 typical cities in China as an example, the spatial distribution of electric vehicle sales and service shops and its influencing factors are empirically explored by spatial econometric methods. The results indicate the following: (1) From high to low, the influence degree of spatial dependence factors are car license restriction policy, the average age of residents, number of schools, built‐up area, the amount of investment in real estate, electric vehicle sales, and GDP; (2) On the whole, the spatial distribution of electric vehicle sales and service shops shows the characteristics of a multi‐center circle‐layered spatial structure that spreads from the inside to the outside; and (3) The spatial influencing factors such as electric vehicle sales, charging stations, permanent resident population, schools, and subway stations in the four first‐tier cities is evident.
Spaces of change: Everyday gender activism through near‐peer gender and sexuality workshops with young people in the UK
Authors: Boyer Kate , Wood Isabelle
Published: June 2023

This paper explores ‘near‐peer’ gender and sexuality workshops taking place in UK secondary schools in support of the nationally required relationships and sex education curriculum. In line with UK law, these workshops promote respect, kindness and communication in a framework that explicitly validates LGBTQI+ identities, relationships and family structures. Based on interviews with 24 gender workshop facilitators, we explore the kind of practices that are needed to create spaces in which young people can openly reflect on their views about gender and sexuality, and advance the above‐noted goals of promoting respect, kindness and empathy for people of all genders and sexualities. We argue that gender workshops constitute an important and under‐explored space of ‘everyday’ activism. We further argue that through practices of atmosphere curation and the near‐peer relational dynamic between facilitators and participants, these workshops create valuable spaces for young people to critically reflect on their views and attitudes about gender and sexuality in a way that does not exist elsewhere. This work extends understanding about the kinds of spatial practices that might be needed to bring forth more positive gender cultures, and deepens our understanding about the experiences and functioning of ‘everyday’ work for social change that takes place outside of ‘high profile’ activist sites such as marches, demonstrations and protests.
Basic haematological tests as inflammatory performance markers of patients treated either by conservative surgery or minor amputation for infected diabetic foot ulcers
Authors: Yammine Kaissar , Abou Orm Ghadi , Mouawad Joseph , Assi Chahine
Published: June 2023

Basic inflammatory markers have been extensively studied to differentiate between non‐infected and infected diabetic foot ulcers (DFUs). Very rarely, basic haematological tests such as white cell count (WCC) and platelet counts were used as performance markers for DFU infection severity. The aim is to investigate these biomarkers in patients with DFU treated exclusively with surgery. In this retrospective comparative study, we included 154 procedures comparing a conservative surgery group (n = 66 for infected DFU) and a minor amputation group (n = 88 for infected DFU with osteomyelitis). Outcomes were set as the preoperative values of: WCC, neutrophils (N), lymphocytes (L), Monocytes (M), Platelets (P), red cell distribution width (RDW) and the ratios N/L, L/M and P/L. Area under curve (AUC) of the receiver operating characteristic (ROC) was calculated based on the diagnosis of minor amputation as a positive result. Cutoff point values with the highest sensitivity and specificity were obtained for each outcome. The highest AUC values were for WCC (0.68), neutrophils (0.68), platelets (0.7) and P/L ratio (0.69) with corresponding cut‐off values of 10,650/mm3, 76%, 234,000/mcL and 265, respectively. The highest sensitivity was for the platelet count (81.5%) while the highest specificity was for L/M (89%) and P/L ratios (87%). Postoperative values showed similar results. Simple routine blood tests could serve as inflammatory performance markers to help predict the severity of infection in patients treated surgically for infected DFU.
Pulsed Impinging Jets for Heat Transfer: A Short Review
Authors: Saliba Georges C. , Batikh Ahmad , Colin Stéphane , Baldas Lucien
Published: June 2023

Because of their effectiveness and adaptability, impinging jets have found their way into a host of applications ranging from cooling turbine blades, to heat management of electronic systems. With constantly increasing demand for efficient and compact cooling systems in certain industries, such as the aerospace or electronics industries, variations on the textbook-setup of an impinging jet have been studied over the years. One such variation introduces a periodic disturbance into the jet that can have an effect on both the structure of flow and the thermal boundary layer at the target surface. In what follows is a short overview of studies having used pulsed jets, often in tandem with other modifications, to try and improve heat transfer. Special attention was also given to the methods by which the periodic disturbance can be produced since they involve setups of different levels of complexity and having different implementation costs.
Numerical Modeling and Experimental Study of Solidification Behavior of Al-Mg2Si Composite Sheet Fabricated Using Continuous Casting Route
Authors: Saini Dheeraj Kumar , Jha Pradeep Kumar
Published: June 2023

Owing to manufacturing challenges, the fabrication of thin sheets of metal matrix composites has been an area of concern for sheet manufacturers. Converting a billet of composite into a sheet using rolling and extrusion is quite energy-intensive and prone to cracking using the conventional casting route. To address this issue, this study explores the development of particle-reinforced near eutectic Al-Mg2Si composite sheets using a vertical twin-roll continuous casting process. The numerical simulation involves fluid flow, solidification, and heat transfer analysis of the twin-roll continuous casting process for producing thin strips. Processing parameters such as the velocity of rolls and superheat temperature of the melt are optimized to successfully convert the melt into a sheet of composite material. A combined numerical and experimental study show that the continuous casting process is more sensitive to the casting speed. A small change in roller speed (2 rpm) significantly affects the solidified fraction at the roller exit. Optimizing the casting speed to 0.072 m/s and inlet temperature to 886 K, an in situ Al-Mg2Si composite sheet of 3 mm thickness is successfully cast. The particle distribution along the casting direction of the sheet is uniform, ensuring the homogeneous mechanical properties reported in terms of hardness. The entire process does not require external stirring to get uniform distribution of the reinforced particles.
Quantum fluctuations spatial mode profiler
Authors: Gabaldon Charris , Barge Pratik , Cuozzo Savannah L. , Novikova Irina , Lee Hwang , Cohen Lior , Mikhailov Eugeniy E.
Published: June 2023

The spatial mode is an essential component of an electromagnetic field description, yet it is challenging to characterize it for optical fields with the low average photon number, such as in a squeezed vacuum. We present a method for the reconstruction of the spatial modes of such fields based on the homodyne measurements of their quadrature noise variance performed with a set of structured masks. We show theoretically that under certain conditions, we can recover individual spatial mode distributions by using the weighted sum of the basis masks, where weights are determined using measured variance values and phases. We apply this approach to analyze the spatial structure of a squeezed vacuum field with various amount of excess thermal noise generated in Rb vapor.
A spectrally discretized wide-angle parabolic equation model for simulating acoustic propagation in laterally inhomogeneous oceans
Authors: Tu Houwang , Wang Yongxian , Zhang Yinuo , Wang Xiaodong , Liu Wei
Published: June 2023

Sound waves can be used to carry out underwater activities. Rapidly and accurately simulating sound propagation is the basis for underwater detection. The wide-angle parabolic model has a good computational speed and accuracy and is currently the main numerical model for mid- and low-frequency sound propagation. The classical wide-angle parabolic equation model is discretized by the finite difference method and a low-order difference scheme is generally adopted. In this paper, a wide-angle parabolic equation model based on a spectral method is proposed. The depth operators of each layer are discretized via the Chebyshev spectral method and then assembled into a global matrix for the forward step. Lateral inhomogeneity is addressed by updating the global depth matrix while stepping forward. In the proposed spectral algorithm, both soft and hard seabeds can be accurately simulated by imposing boundary conditions, and the perfectly matched layer technique is used to truncate the unbounded acoustic half-space. Several representative numerical experiments prove the accuracy and efficiency of the proposed algorithm. However, the spectral method requires that the thickness of the layers does not change during the forward step. Thus, the current spectral algorithm cannot simulate waveguides with terrain undulation, which is its main limitation.
Stability and dynamics of scalar field thin-shell for renormalization group improved Schwarzschild black holes
Authors: Javed Faisal
Published: June 2023

In this paper, our main concern is to obtain the geometrical structure of a thin-shell through the match of inner flat and outer the renormalization group improved Schwarzschild black hole through a well-known cut and paste approach. Then, we are interested to discuss the dynamical configuration of thin-shell composed of a scalar field (massive and massless) through an equation of motion and Klein–Gordon’s equation. Finally, the stable configuration of thin-shell is observed through the linearized radial perturbation approach about equilibrium shell radius with a phantomlike equation of state, i.e., quintessence, dark energy, and phantom energy. It is noted that stable/unstable behavior of thin-shell is found after the expected position of the event horizon of an exterior manifold. It is concluded that the stability of a thin-shell is greater for the choice of Schwarzschild black hole as compared to the renormalized group of improved Schwarzschild black holes.
Fusing In-storage and Near-storage Acceleration of Convolutional Neural Networks
Authors: Okafor Ikenna , Ramanathan Akshay Krishna , Challapalle Nagadastagiri Reddy , Li Zheyu , Narayanan Vijaykrishnan
Published: June 2023

Video analytics has a wide range of applications and has attracted much interest over the years. While it can be both computationally and energy-intensive, video analytics can greatly benefit from in/near memory compute. The practice of moving compute closer to memory has continued to show improvements to performance and energy consumption and is seeing increasing adoption. Recent advancements in solid state drives (SSDs) have incorporated near memory Field Programmable Gate Arrays (FPGAs) with shared access to the drive’s storage cells. These near memory FPGAs are capable of running operations required by video analytic pipelines such as object detection and template matching. These operations are typically executed using Convolutional Neural Networks (CNNs). A CNN is composed of multiple individually processed layers that perform various image processing tasks. Due to lack of resources, a layer may be partitioned into more manageable sub-layers. These sub-layers are then processed sequentially, however, some sub-layers can be processed simultaneously. Moreover, the storage cells within FPGA equipped SSDs are capable of being augmented with in-storage compute to accelerate CNN workloads and exploit the intra-parallelism within a CNN layer. To this end, we present our work, which leverages heterogeneous architectures to create an in/near-storage acceleration solution for video analytics. We designed a NAND flash accelerator and an FPGA accelerator, then mapped and evaluated several CNN benchmarks. We show how to utilize FPGAs, local DRAMs, and in-memory SSD compute to accelerate CNN workloads. Our work also demonstrates how to remove unnecessary memory transfers to save latency and energy.
Towards Attack Detection in Multimodal Cyber-Physical Systems with Sticky HDP-HMM based Time Series Analysis
Authors: Hong Andrew E. , Malinovsky Peter P. , Damodaran Suresh K.
Published: June 2023

Automatic detection of the precise occurrence and duration of an attack reflected in time-series logs generated by cyber-physical systems is a challenging problem. This problem is exacerbated when performing this analysis using logs with limited system information. In a realistic scenario, multiple and differing attack methods may be employed in rapid succession. Modern or legacy systems operate in multiple modes and contain multiple devices recording a variety of continuous and categorical data streams. This work presents a non-parametric Bayesian framework that addresses these challenges using the
sticky Hierarchical Dirichlet Process Hidden Markov Model (sHDP-HMM)
. Additionally, we explore metrics for measuring the accuracy of the detected events, their timings and durations, and compare the computational efficiency of different inference implementations of the model. The efficacy of attack detection is demonstrated in two settings: an avionics testbed and a consumer robot.
Improving Causal Bayesian Networks Using Expertise in Authoritative Medical Ontologies
Authors: Hu Hengyi , Kerschberg Larry
Published: June 2023

Discovering causal relationships among symptoms is a topical issue in the analysis of observational patient datasets. A Causal Bayesian Network (CBN) is a popular analytical framework for causal inference. While there are many methods and algorithms capable of learning a Bayesian network, they are reliant on the complexity and thoroughness of the algorithm and do not consider prior expertise from authoritative sources. This article proposes a novel method of extracting prior causal knowledge contained in Authoritative Medical Ontologies (AMOs) and using this prior knowledge to orient arcs in a CBN learned from observational patient data. Since AMOs are robust biomedical ontologies containing the collective knowledge of the experts who created them, utilizing the ordering information contained within them produces improved CBNs that provide additional insight into the disease domain.

To demonstrate our method, we obtained prior causal ordering information among symptoms from three AMOs: (1) the Medical Dictionary for Regulatory Activities Terminology (MedDRA), (2) the International Classification of Diseases Version 10 Clinical Modification (ICD-10-CM), and (3) Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). The prior ontological knowledge from these three AMOs is then used to orient arcs in a series of CBNs learned from the National Institutes of Mental Health study on Sequenced Treatment Alternatives to Relieve Depression (STAR*D) patient dataset using the Max-Min Hill-Climbing (MMHC) algorithm. Six distinct CBNs are generated using MMHC: an unmodified baseline model using only the algorithm, three CBNs oriented with ordered-variable pairs from MedDRA, ICD-10-CM, and SNOMED CT, and two more with ordered pairs from a combination of these AMOs. The resulting CBNs modified using ordered-variable pairs significantly change the structure of the network. The agreement between the Modified networks and the Baseline ranges from 50% to 90%. A modified network using ordering information from all ontologies obtained an agreement of 50% (10 out of 20 arcs exist in both the Baseline and Modified models) while maintaining comparable predictive accuracy. This indicates that the Modified CBN reflects the causal claims in the AMOs and agrees with both the AMOs and the observational STAR*D dataset. Furthermore, the Modified models discovered new potentially causal relationships among symptoms in the model, while eliminating weaker edges in a qualitative analysis of the significance of these relationships in existing epidemiological research.
Optimization Techniques for Model Checking Leads-to Properties in a Stratified Way
Authors: Do Canh Minh , Phyo Yati , Riesco Adrián , Ogata Kazuhiro
Published: June 2023

We devised the
L
+1-layer divide & conquer approach to leads-to model checking (
L
+1-DCA2L2MC) and its parallel version, and developed sequential and parallel tools for
L
+1-DCA2L2MC. In a temporal logic called
UNITY
, designed by Chandy and Misra, the leads-to temporal connective plays an important role and many case studies have been conducted in UNITY, demonstrating that many systems requirements can be expressed as leads-to properties. Hence, it is worth dedicating to these properties. Counterexample generation is one of the main tasks in the
L
+1-DCA2L2MC technique that can be optimized to improve its running performance. This article proposes a technique to find all counterexamples at once in model checking with a new model checker. Furthermore, layer configuration selection is essential to make the best use of the
L
+1-DCA2L2MC technique. This work also proposes an approach to finding a good layer configuration for the technique with an analysis tool. Some experiments are conducted to demonstrate the power and usefulness of the two optimization techniques, respectively. Moreover, our sequential and parallel tools are compared with SPIN and LTSmin model checkers, showing a promising way to mitigate the state space explosion and improve the running performance of model checking when dealing with large state spaces.
A Survey of Design and Optimization for Systolic Array-based DNN Accelerators
Authors: Xu Rui , Ma Sheng , Guo Yang , Li Dongsheng
Published: June 2023

In recent years, it has been witnessed that the systolic array is a successful architecture for DNN hardware accelerators. However, the design of systolic arrays also encountered many challenges. As DNN structures and applications become more complex, a DNN hardware accelerator based on the typical systolic array architecture suffers severe performance and efficiency penalties. So, it has motivated a significant amount of research on the redesign and optimization of the systolic array architecture. In this article, we survey these works on analyzing, redesigning, and improving the performance and efficiency of the systolic array architecture. These works are critical to the design flow of DNN accelerators based on systolic arrays. We also provide a technique classification of these works on the basis of their main research idea. Further, we attempt to compare the advantages and disadvantages of different designs and different technologies and provide quantitative results for reference. The aim of this survey is to provide researchers with knowledge of the state-of-the-art in the systolic array architecture and motivate them to design highly efficient DNN accelerators of tomorrow.
Discovering Causes of Traffic Congestion via Deep Transfer Clustering
Authors: Wang Mudan , Yuan Yuan , Yan Huan , Sui Hongjie , Zuo Fan , Liu Yue , Li Yong , Jin Depeng
Published: June 2023

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim at discovering the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: (1) traffic congestion is affected by several factors with complex spatio-temporal relations; (2) there are a few samples of congestion data with known causes due to the limitation of human label; (3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: (1) congestion feature extraction module, which extracts the important features distinguishing between different causes of congestion; and (2) congestion cause discovery module, which designs a deep semi-supervised learning based framework to discover the causes of traffic congestion with limited labeled data. Specifically, in pre-training stage, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, in clustering stage, we propose two different clustering methods to discover the congestion causes. For the first clustering method, we extend the classic deep embedded clustering model to produce clusters via soft assignment. For the second one, we iteratively usek-means to group the latent features extracted from the pre-trained model, and use the cluster results as pseudo-labels to fine-tune the network. Extensive experiments show that the performance of our methods is superior to the state-of-the-art baselines, which demonstrates the effectiveness of the proposed cause discovery system. Additionally, our system is deployed and used in the practical production environment at Amap.
Information-Theoretic Approaches to Differential Privacy
Authors: Ünsal Ayşe , Önen Melek
Published: June 2023

This tutorial studies relations between differential privacy and various information-theoretic measures by using several selective articles. In particular, we present how these connections can provide new interpretations for the privacy guarantee in systems that deploy differential privacy in an information-theoretic framework. Accordingly, the tutorial delivers an extensive summary on the existing literature that makes use of information-theoretic measures and tools such as mutual information, min-entropy, Kullback-Leibler divergence, and rate-distortion function for quantification and characterization of differential privacy in various settings.
A Survey on Thwarting Memory Corruption in RISC-V
Authors: Brohet Marco , Regazzoni Francesco
Published: June 2023

With embedded devices becoming more pervasive and entrenched in society, it is paramount to keep these systems secure. A threat plaguing these systems consists of software vulnerabilities that cause memory corruption, potentially allowing an attacker to breach the device. Software-based countermeasures exist, but suffer from high overhead. In this survey, we investigate whether this could be mitigated using dedicated hardware. Driven by the advancements of open hardware, we focus on implementations for RISC-V, a novel and open architecture tailored for customization. We distinguish between methods validating memory accesses beforehand, obfuscating information necessary for an attack, and detecting memory values corrupted earlier. We compare on qualitative metrics, such as the security coverage and level of transparency, and performance in both software and hardware. Although current implementations do not easily allow for a fair comparison as their evaluation methodologies vary widely, we show that current implementations are suitable to minimize the runtime overhead with a relatively small area overhead. Nevertheless, we identified that further research is still required to mitigate more fine-grained attacks such as intra-object overflows, to integrate into more sophisticated protected execution environments towards resilient systems that are automatically recoverable, and to move towards more harmonized evaluation.
Mobile Edge Computing and Machine Learning in the Internet of Unmanned Aerial Vehicles: A Survey
Authors: Ning Zhaolong , Hu Hao , Wang Xiaojie , Guo Lei , Guo Song , Wang Guoyin , Gao Xinbo
Published: June 2023

Unmanned Aerial Vehicles (UAVs) play an important role in the Internet of Things and form the paradigm of the Internet of UAVs, due to their characteristics of flexibility, mobility, and low costs. However, resource constraints such as dynamic wireless channels, limited battery capacities, and computation resources of UAVs make traditional methods inefficient in the Internet of UAVs. The thriving of Mobile Edge Computing (MEC) and Machine Learning (ML) is of great significance and is promising for real-time resource allocation, trajectory design, and intelligent decision making. This survey provides a comprehensive review of key technologies, applications, solutions, and challenges based on the integration of MEC and ML in the Internet of UAVs. First, key technologies of MEC and ML are presented. Then, their integration and major issues in the Internet of UAVs are presented. Furthermore, the applications of MEC and ML in the Internet of UAVs under urban, industrial, and emergency scenarios are discussed. After that, this survey summarizes the current solutions for MEC and ML in the Internet of UAVs based on the considered issues. Finally, some open problems and challenges are discussed.
Profitability Analysis of Small-Scale Cage Aquaculture Farms in the Volta Lake of Ghana
Authors: Magna Emmanuel Kaboja , Mensah Emmanuel Tetteh-Doku , Mabe Franklin Nantui , Johnson-Ashun Mercy , Konadu Lilly Osei , Appiah Ebenezer Koranteng
Published: June 2023

In Ghana, aquaculture offers acceptable opportunities for generating income. Cage aquaculture has a lot of potential for growth in Lake Volta. In this region, cage aquaculture farmers perform aquaculture in a variety of ways that fall into three major groups: commercial, medium scale, and small scale. In this study, the profitability and production economics of small-scale aquaculture operations in Lake Volta were examined. The average total revenue accrued and the average total costs of production were Gh₵395231.25 and Gh₵267970.15, respectively. The overall gross margin and net return for all the farms were Gh₵130294.02 and Gh₵127,261.10 per cycle, respectively. The overall assessment of the profitability performance indicators such as the benefit-cost ratio (BCR), the return on investment (ROI), the net present value (NPV), the operating expense ratio (OE), the operating profit margin (OPM), and the gross margin ratio (GMR) were 1.47, 47.49%, Gh₵407625.47, 0.77%, 32.20%, and 33.00%, respectively. These indicators showed that small-scale cage fish farming in the study area is profitable. The sensitivity analysis further demonstrated that small-scale cage fish production was robustly profitable. In light of this, the study suggests that stakeholders educate small-scale cage aquaculture farmers on the profitability of the business and make a concerted effort to teach and equip farmers with best management practises (BMPs), water quality management, feeds, and feeding management.
Effect of High Hydrostatic Pressure and Thermal Processing on the Shelf Life and Quality Attributes of Apple-Kiwi-Carrot Puree Blend
Authors: Tian Xuezhi , Yang Yunshu , Zhang Wenjing , Wang Yongtao
Published: June 2023

The effects of high hydrostatic pressure (HHP) (400 MPa/2 min and 500 MPa/2 min) and thermal processing (TP) (90°C/2 min) on the microorganisms, nutrition, color, endogenous enzyme activities, antioxidant capacity, and rheological properties of blended apple-kiwi-carrot puree were comprehensively evaluated after processing and during storage at 4°C. Results showed the microbiological shelf life of the HHP or TP products was at least 24 days. TP inactivated polyphenol oxidase activity more effectively, retaining more polyphenols in samples during storage, whereas HHP treatments were more beneficial for the preservation of ascorbic acid, total carotenoids, β-carotene, and antioxidant capacity. During storage, there was no significant difference in PME activity among all treated samples, but particle size distribution and viscoelasticity tests demonstrated that the 400 MPa treated samples were more stable in their textures. Moreover, the principal component analysis (PCA) intuitively revealed that the overall sensory and nutritional attributes of the HHP-treated samples were closer to the fresh product. These results demonstrated that HHP could be a better choice than TP for use in the production of high-quality puree blends.
Cookies Enriched with Maqui (Aristotelia chilensis (Mol.) Stuntz) Flour: Good Source of Dietary Fiber and Antioxidant Capacity
Authors: Bastias-Montes José-Miguel , Orrego-Aguilera María-Fernanda , Ortiz-Espinoza Francisca-Jesus , Ortiz-Viedma Jaime-Alejandro , Acuña-Nelson Sergio-Miguel
Published: June 2023

Byproducts derived from the agricultural industry and food processing, such as seeds and skin or husks, have become important due to environmental sustainability and their value-added potential. Maqui (Aristotelia chilensis (Mol.) Stuntz) is a native Chilean wild tree that has several beneficial properties due to its high content of anthocyanins, anthocyanidins, phenolic acids, tannins, and fiber. The present study involved flour derived from maqui byproducts (seeds and skin) to produce cookies with a higher content of nutrients, dietary fiber, and antioxidants. Maqui bagasse was converted into flour and mixed with wheat flour to prepare the cookies. The control consisted of wheat flour cookies with no added maqui flour. Results indicated that the cookies with the added maqui flour were preferred more than the control and had a higher percentage of fiber. Compared with the control, the amount of anthocyanins increased 3.5 times, and the amount of antioxidants, measured via the DPPH (2,2

′

-diphenyl-1-picrylhydrazyl radical) assay, increased approximately 10 times. Our results show that the incorporation of maqui byproducts, in the form of maqui flour, into the cookie formulation is an effective way to add nutritional value to cookies by increasing dietary fiber, total polyphenols, and total anthocyanins.
Application‐Layer DDoS Attack Detection Using Explicit Duration Recurrent Network‐Based Application‐Layer Protocol Communication Models
Authors: Xie Bailin , Wang Yu , Wen Guogui , Xu Xiaojun
Published: June 2023

Existing application‐layer distributed denial of service (AL‐DDoS) attack detection methods are mainly targeted at specific attacks and cannot effectively detect other types of AL‐DDoS attacks. This study presents an application‐layer protocol communication model for AL‐DDoS attack detection, based on the explicit duration recurrent network (EDRN). The proposed method includes model training and AL‐DDoS attack detection. In the AL‐DDoS attack detection phase, the output of each observation sequence is updated in real time. The observation sequences are based on application‐layer protocol keywords and time intervals between adjacent protocol keywords. Protocol keywords are extracted based on their identification using regular expressions. Experiments are conducted using datasets collected from a real campus network and the CICDDoS2019 dataset. The results of the experiments show that EDRN is superior to several popular recurrent neural networks in accuracy, F1, recall, and loss values. The proposed model achieves an accuracy of 0.996, F1 of 0.992, recall of 0.993, and loss of 0.041 in detecting HTTP DDoS attacks on the CICDDoS2019 dataset. The results further show that our model can effectively detect multiple types of AL‐DDoS attacks. In a comparison test, the proposed method outperforms several state‐of‐the‐art approaches.
On Uncertainty Measures of the Interval-Valued Hesitant Fuzzy Set
Authors: Xu Yingjun
Published: June 2023

Interval-valued hesitant fuzzy sets (IVHFS), as a kind of decision information presenting tool which is more complicated and more scientific and more elastic, have an important practical value in multiattribute decision-making. There is little research on the uncertainty of IVHFS. The existing uncertainty measure cannot distinguish different IVHFS in some contexts. In my opinion, for an IVHFS, there should exist two types of uncertainty: one is the fuzziness of an IVHFS and the other is the nonspecificity of the IVHFS. To the best of our knowledge, the existing index to measure the uncertainty of IVHFS all are single indexes, which could not consider the two facets of an IVHFS. First, a review is given on the entropy of the interval-valued hesitant fuzzy set, and the fact that existing research cannot distinguish different interval-valued hesitant fuzzy sets in some circumstances is pointed out. With regard to the uncertainty measures of the interval-valued hesitant fuzzy set, we propose a two-tuple index to measure it. One index is used to measure the fuzziness of the interval-valued hesitant fuzzy set, and the other index is used to measure the nonspecificity of it. The method to construct the index is also given. The proposed two-tuple index can make up the fault of the existing interval-valued hesitant fuzzy set’s entropy measure.
Meta‐Learning‐Based Spatial‐Temporal Adaption for Coldstart Air Pollution Prediction
Authors: Wu Zhiyuan , Liu Ning , Li Guodong , Liu Xinyu , Wang Yue , Zhang Lin
Published: June 2023

Air pollution is a significant public concern worldwide, and accurate data‐driven air pollution prediction is crucial for developing alerting systems and making urban decisions. As more and more cities establish their monitoring networks, there is a pressing need for coldstart model training with limited data accumulation in new cities. However, traditional spatial‐temporal modeling and transfer learning schemes have been challenged under this scenario because of insufficient usage of available source data and suboptimal transferring strategy. To address these issues, we propose a meta‐learning‐based spatial‐temporal adaptation solution for coldstart air pollution prediction. Our approach is a model‐agnostic framework that enables a given backbone predictor with adaption ability across different space and time locations. Specifically, it learns a factorization of the available source data distribution and recognizes the target city as one of its components, greatly reducing the data accumulation requirement and providing coldstart capability. Furthermore, we design a novel bidirectional meta‐learner that can simultaneously leverage task embeddings learned from data and features constructed based on prior knowledge. We conduct comprehensive experiments on both synthetic and real‐world air pollution datasets of four distinct pollutants. The results demonstrate that our proposed method achieves a 5.2% lower 24‐hour prediction mean absolute error (MAE) than pretraining and fine‐tuning solutions when facing a new city with only 200 hours of data, which empirically verifies the effectiveness of our approach as a coldstart training solution.
Distributed Space Shift Keying in Cooperative Wireless Networks: Capacity Analysis and Error Performance
Authors: Khidr Zahra Azad , Mustafa Samah Abdulakreem
Published: June 2023

In this study, a distributed fashion of space shift keying as an efficient cooperative framework is proposed and studied to address the main challenges in future cellular networks by providing high energy efficiency and meeting the demand growth with the lowest complexity and the highest performance. A comprehensive mathematical analysis has been performed to find a closed form for the channel capacity of distributed space shift keying and to depict the influence of the number of distributed antennas on the achievable channel capacity. In addition, a simulation study of the error performance is presented using two detection methods: maximum-likelihood detection based on channel state information and blind detection. It was shown that as the number of cooperating relays increases, the error performance of the detectors converges, although they incur different costs and computational complexities. A comparison study in terms of error performance and capacity is applied with other cooperative spatial modulation to portray the outperformance of the system presented in this work.
LncRNA AC040162.3 Promotes HCV‐Induced T2DM Deterioration through the miRNA‐223‐3p/NLRP3 Molecular Axis
Authors: Niu Ben , Xia Xueshan , Ma Lijing , Yao Lixuan , Zhang Yating , Su Heng
Published: June 2023

Background. Diabetes is one of the most common diseases and major public health burdens worldwide. Type 2 diabetes mellitus (T2DM) is associated with chronic hepatitis C virus (HCV) infection, and lncRNAs play an important role in HCV‐induced T2DM. We aimed to explore the effect of lncRNA AC040162.3 on HCV‐induced T2DM. Methods. HCV was used to infect MIN6 cells to establish an in vitro model. HCV copy number and miRNA expression were detected by Real Time Quantitative PCR (RT‐qPCR). Enzyme‐Linked Immunosorbent Assay (ELISA) was used to detect the secretion of insulin, and methyl thiazolyl tetrazolium (MTT) was applied to analyze cell viability. Apoptosis was analyzed by Western blotting and flow cytometry. In addition, Western blotting and TdT‐mediated dUTP Nick End Labeling (TUNEL) were used to analyze pyroptosis. Luciferase reporter assays were used to investigate the targeting relationship. Results. The expression of LncRNA AC040162.3 and NLRP3 was markedly increased in HCV–T2DM, while the expression of miR‐223‐3p was remarkably inhibited. In vitro experiments demonstrated that lncRNA AC040162.3 silencing or miR‐223‐3p overexpression remarkably alleviated HCV‐induced T2DM deterioration by inhibiting cell apoptosis and pyroptosis and enhancing cell viability. We then demonstrated that silencing lncRNA AC040162.3 promoted the expression of miR‐223‐3p and that miR‐223‐3p bound to lncRNA AC040162.3 and the NLRP3 binding site. In addition, the protective effects of LncRNA AC040162.3 silencing in HCV‐infected MIN6 cells were reversed by overexpression of NLRP3 or silencing of miR‐223‐3p. Conclusion. Silencing of lncRNA AC040162.3 alleviates the process of HCV‐induced T2DM by governing the miR‐223‐3p/NLRP3 axis.
Switched-Resistor Passive Balancing of Li-Ion Battery Pack and Estimation of Power Limits for Battery Management System
Authors: Kumar Sonu , Rao S. Koteswara , Singh Arvind R. , Naidoo Raj
Published: June 2023

The battery pack performance and expected lifespan are crucial in electric vehicle applications. Balancing the charge on a battery pack connected in series and parallel is crucial due to manufacturing discrepancies and distinct performance of each cell in a standard battery pack. In this paper, a switched-resistor passive balancing-based method is proposed for balancing cells in a battery management system (BMS). The value of the available voltage at the battery cell terminals is balanced using resistors in an electrical circuit, and the excess voltage is eliminated. The cell balancing outcome demonstrates that the electrical circuit can maintain an even voltage across each cell. The procedure of balancing involves individually adjusting each cell’s level of charge. Passive balancing releases energy as heat by draining charge from cells that have too much charge. A passive cell balancer is a cost-effective solution and easy to install, but due to thermal loss from a resistor, it has a low energy efficiency for cell balancing and necessitates a lengthy balancing process. This passive cell balancer is an effective and reliable method for low-power devices and portable applications such as electrical vehicles. The power limits during charging and discharging are estimated using the bisection method.
Influence of Fir-Tree Tenon Blade Parameters on Blade Dynamic Characteristics
Authors: Yang Zhenya , Li Chaofeng
Published: June 2023

In this paper, based on the theory of elastic structure analysis, considering the rotational softening and centrifugal stiffening of the blade, the dynamic model of the fir-tree rotating blade and the macroslip hysteresis model of the dry friction on the contact surface of fir-tree tenon are established. Based on elastic mechanics, the pressure distribution model of fir-tree tenon is innovatively established and the relationship between fit clearance and contact pressure has been derived. After the blade is discretized by the finite element method, the dynamic equation of the blade is solved iteratively by the Newmark-β numerical integration method. According to the dynamic equation of the blade, the influence of the change of the tenon angle on the forced response and contact stress of the blade is derived. Finally, some novel conclusions are obtained by analyzing the influence of pressure distribution, tenon angle, friction coefficient, rotational speed, and aerodynamic excitation on blade vibration characteristics. It provides a scheme and relevant basis for the design and development of tenon blades with the most damping effect at different speeds in industrial practice.