bluuebunny/crossref_metadata_2025 huggingface dataset:
From In Silico Simulation between TGF-β Receptors and Quercetin to Clinical Insight of a Medical Device Containing Allium cepa: Its Efficacy and Tolerability on Post-Surgical Scars
Authors: Cosio Terenzio , Costanza Gaetana , Coniglione Filadelfo , Romeo Alice , Iacovelli Federico , Diluvio Laura , Dika Emi , Shumak Ruslana Gaeta , Rossi Piero , Bianchi Luca , Falconi Mattia , Campione Elena
Published: August 2023

(1) Objective: Keloid and hypertrophic scars are a challenge in clinical management, causing functional and psychological discomfort. These pathological scars are caused by a proliferation of dermal tissue following skin injury. The TGF-β/Smad signal pathway in the fibroblasts and myofibroblasts is involved in the scarring process of skin fibrosis. Today, multiple therapeutic strategies that target the TGF-β/Smad signal pathway are evaluated to attenuate aberrant skin scars that are sometimes difficult to manage. We performed a head-to-head, randomized controlled trial evaluating the appearance of the post-surgical scars of 64 subjects after two times daily topical application to compare the effect of a class I pullulan-based medical device containing Allium cepa extract 5% and hyaluronic acid 5% gel versus a class I medical device silicone gel on new post-surgical wounds. (2) Methods: Objective scar assessment using the Vancouver Scar Scale (VSS), POSAS, and other scales were performed after 4, 8, and 12 weeks of treatment and statistical analyses were performed. The trial was registered in clinicalTrials.gov ( NCT05412745). In parallel, molecular docking simulations have been performed to investigate the role of Allium cepa in TGF-β/Smad signal pathway. (3) Results: We showed that VSS, POSAS scale, itching, and redness reduced significantly at week 4 and 8 in the subjects using devices containing Allium cepa and HA. No statistically significant differences in evaluated scores were noted at 12 weeks of treatment. Safety was also evaluated by gathering adverse events related to the application of the gel. Subject compliance and safety with the assigned gel were similar between the two study groups. Molecular docking simulations have shown how Allium cepa could inhibit fibroblasts proliferation and contraction via TGF-β/Smad signal pathway. (4) Conclusions: The topical application of a pullulan-based medical device containing Allium cepa and HA showed a clear reduction in the local inflammation, which might lead to a reduced probability of developing hypertrophic scars or keloids.
Molecular Cytology of ‘Little Animals’: Personal Recollections of Escherichia coli (and Bacillus subtilis)
Authors: Nanninga Nanne
Published: August 2023

This article relates personal recollections and starts with the origin of electron microscopy in the sixties of the previous century at the University of Amsterdam. Novel fixation and embedding techniques marked the discovery of the internal bacterial structures not visible by light microscopy. A special status became reserved for the freeze-fracture technique. By freeze-fracturing chemically fixed cells, it proved possible to examine the morphological effects of fixation. From there on, the focus switched from bacterial structure as such to their cell cycle. This invoked bacterial physiology and steady-state growth combined with electron microscopy. Electron-microscopic autoradiography with pulses of [3H] Dap revealed that segregation of replicating DNA cannot proceed according to a model of zonal growth (with envelope-attached DNA). This stimulated us to further investigate the sacculus, the peptidoglycan macromolecule. In particular, we focused on the involvement of penicillin-binding proteins such as PBP2 and PBP3, and their role in division. Adding aztreonam (an inhibitor of PBP3) blocked ongoing divisions but not the initiation of new ones. A PBP3-independent peptidoglycan synthesis (PIPS) appeared to precede a PBP3-dependent step. The possible chemical nature of PIPS is discussed.
Idiosyncratic DILI and RUCAM under One Hat: The Global View
Authors: Teschke Rolf , Danan Gaby
Published: August 2023

Drugs are prescribed worldwide to treat diseases but with the risk of idiosyncratic drug-induced liver injury (iDILI). The most important difficulty is how best to establish causality. Based on strong evidence and principles of artificial intelligence (AI) to solve complex processes through quantitative algorithms using scored elements, progress was achieved with the Roussel Uclaf Causality Assessment Method (RUCAM) in its original and updated versions, often viewed now as the gold standard. As a highly appreciated diagnostic algorithm, the RUCAM is in global use with around 100,000 iDILI cases published worldwide using RUCAM to assess causality, largely outperforming any other specific causality assessment tool in terms of case numbers. Consequently, the RUCAM helps to establish a list of top-ranking drugs worldwide implicated in iDILI and to describe clinical and mechanistic features of iDILI caused by various drugs. In addition, the RUCAM was recently applied in iDILI cases of patients treated for coronavirus disease 2019 (COVID-19) infections or cancer patients treated with immune checkpoint inhibitors (ICIs), as well as in the search for new treatment options with conventional drugs in iDILI. Analyses of RUCAM-based iDILI cases are helpful to support pathogenetic steps like immune reactions, genetic predisposition as evidenced by human leucocyte antigens (HLA) genotypes for selected drugs, and the role of the gut microbiome. To achieve consistency in data collection, analysis, and specific clinical and pathogenetic presentation, researchers, regulatory agencies, and pharmaceutical firms should place iDILI and the updated RUCAM as the causality tool under one and the same hat in review articles and clinical guidelines for the diagnosis and treatment of iDILI.
Durability and Sustainability of Cement and Concrete Composites
Authors: Yang Jin , He Xingyang
Published: August 2023

Durability and sustainability are important objectives within the development of cement and concrete composites [...]
Application of Anodic Titanium Oxide Modified with Silver Nanoparticles as a Substrate for Surface-Enhanced Raman Spectroscopy
Authors: Czerwiński Mateusz , del Olmo Martinez Ruben , Michalska-Domańska Marta
Published: August 2023

The formation of nanostructured anodic titanium oxide (ATO) layers was explored on pure titanium by conventional anodizing under two different operating conditions to form nanotube and nanopore morphologies. The ATO layers were successfully developed and showed optimal structural integrity after the annealing process conducted in the air atmosphere at 450 °C. The ATO nanopore film was thinner (1.2 +/− 0.3 μm) than the ATO nanotube layer (3.3 +/− 0.6 μm). Differences in internal pore diameter were also noticeable, i.e., 88 +/− 9 nm and 64 +/− 7 nm for ATO nanopore and nanotube morphology, respectively. The silver deposition on ATO was successfully carried out on both ATO morphologies by silver electrodeposition and Ag colloid deposition. The most homogeneous silver deposit was prepared by Ag electrodeposition on the ATO nanopores. Therefore, these samples were selected as potential surface-enhanced Raman spectroscopy (SERS) substrate, and evaluation using pyridine (aq.) as a testing analyte was conducted. The results revealed that the most intense SERS signal was registered for nanopore ATO/Ag substrate obtained by electrodeposition of silver on ATO by 2.5 min at 1 V from 0.05M AgNO3 (aq.) (analytical enhancement factor, AEF ~5.3 × 104) and 0.025 M AgNO3 (aq.) (AEF ~2.7 × 102). The current findings reveal a low-complexity and inexpensive synthesis of efficient SERS substrates, which allows modification of the substrate morphology by selecting the parameters of the synthesis process.
On the Use of Metal Sinter Powder in Laser Powder Bed Fusion Processing (PBF-LB/M)
Authors: Bernsmann Jan-Simeon Ludger , Hillebrandt Simon , Rommerskirchen Max , Bold Sebastian , Schleifenbaum Johannes Henrich
Published: August 2023

Metal Laser Powder Bed Fusion (PBF-LB/M Powder Bed Fusion, Laser-Based/Metal) offers decisive advantages over conventional manufacturing processes. Complex geometries can be produced that cannot, or only to a limited extent, be manufactured with conventional manufacturing processes. One of the main disadvantages of the process are high investment and operating costs. In order to make the PBF-LB/M process accessible to new research areas, the costs need to be reduced. Therefore, this work investigates whether laser beam sources and motion systems in currently established PBF-LB/M systems can be replaced by more cost-effective components. To reduce the operating costs for PBF-LB/M, the studies are carried out based on previous work with water-atomized, process-foreign sinter powder instead of gas-atomized, spherical PBF-LB/M powders. A cost-efficient, low-alloyed powder is selected (Höganäs HP1) and processed on two different PBF-LB/M machines with a restricted process window using process parameter values that current low-cost machines can achieve. The results show that a multimode fiber laser leads to a more stable process and wider melt pools compared to a single mode fiber laser. In addition, a lower sensitivity of the process with respect to modified process parameters is observed for the multimode laser, resulting in a wider range of stable process windows. A Cartesian motion system (gantry) is suitable for use in PBF-LB/M despite lower scan speeds compared to galvanometer scanners. Beam guidance in the XY-plane offers new possibilities for machine and process design that are not possible with usual scanner systems.
Properties of Gangue Powder Modified Fly Ash-Based Geopolymer
Authors: Zhang Tianhao , Yang Zhenghui , Zhang Dongsheng , Yang Qiuning
Published: August 2023

The environmental and economic problems caused by gangue accumulation continue to worsen. Therefore, the implementation of a cost-effective method for utilizing gangue resources is urgent. In this study, different gangue powder (GP) contents (0%, 10%, 20%, 30%, 40%, and 50%) for mechanical–thermal activation were used to modify a fly ash-based geopolymer (FAG). Further, the effect of GP was revealed by investigating the setting time, fluidity, porosity, water absorption rate, mechanical properties, drying shrinkage, and microstructure. Results showed that the addition of GP reduced the fluidity and setting time of gangue powder—fly ash-base geopolymer (GPFAG), improved density, and decreased the water absorption rate of GPFAG. Moreover, its mechanical properties gradually improved. Compared with GPFAG0 (FAG with 0% GP), the 28-d compressive and flexural strengths of GPFAG50 (FAG with 50% GP) increased by 246.4% and 136.8%, respectively. The incorporation of GP increased the drying shrinkage. The results of XRD and FTIR analyses showed that the addition of GP increased the production of amorphous silica–aluminate gels, such as N-S-A-H and C-S-A-H. Moreover, strong Si-O-T vibrational peaks appeared in the range 743–1470 cm−1, characterizing the GPFAG strength and reaction degree.
Research on Lateral Maneuverability of a Supercavitating Vehicle Based on RBFNN Adaptive Sliding Mode Control with Rolling Restriction and Planing Force Avoidance
Authors: Yang Guang , Lu Faxing , Xu Junfei
Published: August 2023

This paper addresses the lateral motion control of a supercavitating vehicle and studies its ability to maneuver. According to the unique hydrodynamic characteristics of the supercavitating vehicle, highly coupled nonlinear 6-degree-of-freedom (DOF) dynamic and kinematic models are constructed considering time-delay effects. A control scheme utilizing radial basis function (RBF) neural-network-(NN)-based adaptive sliding with planing force avoidance is proposed to simultaneously control the longitudinal stability and lateral motion of the supercavitating vehicle in the presence of external ocean-induced disturbances. The online estimation of nonlinear disturbances is conducted in real time by the designed NN and compensated for the dynamic control laws. The adaptive laws of the NN weights and control parameters are introduced to improve the performance of the NN. The least squares method is utilized to solve the actuator control efforts with rolling restriction in real-time online. Rigorous theoretical proofs based on the Lyapunov theory prove the globally asymptotic stability of the proposed controller. Finally, numerical simulations were performed to obtain maximum maneuverability and verify the effectiveness and robustness of the proposed control scheme.
Two-Threshold-Variable Integer-Valued Autoregressive Model
Authors: Zhang Jiayue , Zhu Fukang , Chen Huaping
Published: August 2023

In the past, most threshold models considered a single threshold variable. However, for some practical applications, models with two threshold variables may be needed. In this paper, we propose a two-threshold-variable integer-valued autoregressive model based on the binomial thinning operator and discuss some of its basic properties, including the mean, variance, strict stationarity, and ergodicity. We consider the conditional least squares (CLS) estimation and discuss the asymptotic normality of the CLS estimator under the known and unknown threshold values. The performances of the CLS estimator are compared via simulation studies. In addition, two real data sets are considered to underline the superior performance of the proposed model.
Positive Solutions to the Discrete Boundary Value Problem of the Kirchhoff Type
Authors: Lin Bahua , Zhou Zhan
Published: August 2023

The paper aims to study a discrete boundary value problem of the Kirchhoff type based on the critical point theory and the strong maximum principle. Compared to the existing literature, the existence and multiplicity of positive solutions to the problem are considered according to the behavior of the nonlinear term f in some points between the zero and positive infinity, which is a new attempt. Under different assumptions of the nonlinear term f, we obtain the determined open intervals of the parameter λ, such that the problem has at least three positive solutions or at least two positive solutions in different intervals. In the end, two concrete examples are used to illustrate our main conclusions.
A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes
Authors: Wu Cang , Hou Huijuan , Lei Chunli , Zhang Pan , Du Yongjun
Published: August 2023

Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.
A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory
Authors: Liu Jiaqi , Xu Hucheng , Deng Xiaoheng , Liu Hui , Li Deng
Published: August 2023

Crowdsensing uses the participants’ smart devices to form a new perception network. The coverage of crowdsensing’s tasks determines the quality of services. Under the constraint of budget and the number of participants, the platform needs to increase the participation duration of participants through incentive mechanisms to increase the coverage of tasks. There are two problems with the existing incentive mechanisms: (1) many incentives ignore the participants’ characteristics, and using a single incentive mechanism for different participants will make the incentive effect not reach the expectation; (2) many incentives will affect the effectiveness because of the decision problem caused by asymmetric information. Inspired by ensemble learning and prospect theory, this paper proposes the Incentive Mechanism based on Ensemble Learning and Prospect Theory (IMELPT). First, we propose the Deep-Stacking-Generation algorithm based on Dropout (DSGD), to predict the participants and distinguish whether they are long-term or short-term participants. If the participants are short-term, we incentivize them through the Short-term Participant Incentive Mechanism based on Prospect Theory (SPIMPT). We increase the participation duration by transforming the change in reward into asymmetric information that aligns the participant’s goal with the platform. If the participants are long-term participants, we motivate them through the Long-term Participant Incentive Mechanism (LPIM), to maintain the participation rate of participants by maximizing their utility. Theoretical analysis and experiments on real datasets demonstrated that IMELPT can reliably improve the coverage of crowdsensing tasks.
Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions
Authors: Shepelev Vladimir , Glushkov Aleksandr , Slobodin Ivan , Balfaqih Mohammed
Published: August 2023

This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold.
Robust Model Predictive Control for Two-DOF Flexible-Joint Manipulator System
Authors: Li Rong , Wang Hengli , Yan Gaowei , Li Guoqiang , Jian Long
Published: August 2023

This paper presents a practical study on how to improve the ℋ∞ performance and meet the input–output constraints of the two-degrees-of-freedom (DOF) flexible-joint manipulator system (FJMS) with parameter uncertainties and external disturbances. For this reason, a robust constrained moving-horizon ℋ∞ controller is designed to improve the system ℋ∞ performance while still satisfying the input–output constraints of the uncertain system. First, the uncertain controlled system model of the two-DOF FJMS is established via the Lagrange equation method, Spong’s assumption, and the linear fractional transformation (LFT) technique. Then, the control requirements and input–output constraints of the uncertain system are transformed into the linear matrix inequality (LMI) via the theory of ℋ∞ control and the full-block multiplier technique. Next, the LMI optimization problem refreshed by the current state is addressed at each sample moment with the idea of the moving-horizon control of the model predictive control (MPC), and the calculated gain is implemented to the nonlinear closed-loop system under the state feedback structure. The validity and feasibility of the designed control scheme is finally verified via the results of simulation experiments.
Spectral Applications of Vertex-Clique Incidence Matrices Associated with a Graph
Authors: Fallat Shaun , Mojallal Seyed Ahmad
Published: August 2023

Using the notions of clique partitions and edge clique covers of graphs, we consider the corresponding incidence structures. This connection furnishes lower bounds on the negative eigenvalues and their multiplicities associated with the adjacency matrix, bounds on the incidence energy, and on the signless Laplacian energy for graphs. For the more general and well-studied set S(G) of all real symmetric matrices associated with a graph G, we apply an extended version of an incidence matrix tied to an edge clique cover to establish several classes of graphs that allow two distinct eigenvalues.
Sizing, Modeling, and Performance Comparison of Squirrel-Cage Induction and Wound-Field Flux Switching Motors
Authors: Abunike Chiweta E. , Akuru Udochukwu B. , Okoro Ogbonnaya I. , Awah Chukwuemeka C.
Published: August 2023

In this study, the analytical design and electromagnetic performance comparison of a squirrel-cage induction motor (SCIM) and a wound-field flux switching motor (WFFSM) for high-speed brushless industrial motor drives is undertaken for the first time. The study uses analytical sizing techniques and finite element analysis (FEA) to model and predict the performance of both motors at a 7.5 kW output power. This study includes detailed equations and algorithms for sizing and modeling of both types of motors, as well as performance calculations that aid in motor selection, design optimization, and system integration. The main findings show that the SCIM has superior torque performance for starting and overload conditions, while the WFFSM offers advantages in power factor, efficiency over a wide operating range, and potential for higher peak power output. To this end, the WFFSM is capable of high-speed and high-efficiency operation while the SCIM is suitable for applications requiring variable speed operation. The validation study shows good agreement between analytical and FEA calculations for both motors. The results provide insights into the design and performance characteristics of both motors, enabling researchers to explore innovative approaches for improving their efficiency, reliability, and overall performance.
Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction
Authors: Ismail Walaa N. , Alsalamah Hessah A.
Published: August 2023

The prediction of skin cancer poses a number of challenges due to the differences in visual characteristics between melanoma, basal cell carcinomas, and squamous cell carcinomas. These visual differences pose difficulties for models in discerning subtle features and patterns accurately. However, a remarkable breakthrough in image analysis using convolutional neural networks (CNNs) has emerged, specifically in the identification of skin cancer from images. Unfortunately, manually designing such neural architectures is prone to errors and consumes substantial time. It has become increasingly popular to design and fine-tune neural networks by using metaheuristic algorithms that are based on natural phenomena. A nature-inspired algorithm is a powerful alternative to traditional algorithms for solving problems, particularly in complex optimization tasks. One such algorithm, the Harris hawk optimization (HHO), has demonstrated promise in automatically identifying the most appropriate solution across a wide range of possibilities, making it suitable for solving complex optimization problems. The purpose of this study is to introduce a novel automated architecture called “HHOForSkin” that combines the power of convolutional neural networks with meta-heuristic optimization techniques. The HHOForSkin framework uses an innovative custom CNN architecture with 26 layers for the analysis of medical images. In addition, a Harris hawk optimization algorithm (HHO) is used to fine-tune the developed model for multiple skin cancer classification problems. The developed model achieves an average accuracy of 99.1% and 98.93% F1 score using a publicly available skin cancer dataset. These results position the developed optimization-based skin cancer detection strategy at the forefront, offering the highest accuracy for seven-class classification problems compared to related works.
Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction
Authors: Noh Yoonjae , Kim Jong-Min , Hong Soongoo , Kim Sangjin
Published: August 2023

The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid development of data engineering, a situation has arisen wherein extensive amounts of information must be processed at finer time intervals. Addressing the prevalent issues of difficulty in handling multivariate high-frequency time-series data owing to multicollinearity, resource problems in computing hardware, and the gradient vanishing problem due to the layer stacking in recurrent neural network (RNN) series, a novel algorithm is developed in this study. For financial market index prediction with these highly complex data, the algorithm combines ResNet and a variable-wise attention mechanism. To verify the superior performance of the proposed model, RNN, long short-term memory, and ResNet18 models were designed and compared with and without the attention mechanism. As per the results, the proposed model demonstrated a suitable synergistic effect with the time-series data and excellent classification performance, in addition to overcoming the data structure constraints that the other models exhibit. Having successfully presented multivariate high-frequency time-series data analysis, this study enables effective investment decision making based on the market signals.
Birational Quadratic Planar Maps with Generalized Complex Rational Representations
Authors: Wang Xuhui , Han Yuhao , Ni Qian , Li Rui , Goldman Ron
Published: August 2023

Complex rational maps have been used to construct birational quadratic maps based on two special syzygies of degree one. Similar to complex rational curves, rational curves over generalized complex numbers have also been constructed by substituting the imaginary unit with a new independent quantity. We first establish the relationship between degree one, generalized, complex rational Bézier curves and quadratic rational Bézier curves. Then we provide conditions to determine when a quadratic rational planar map has a generalized complex rational representation. Thus, a rational quadratic planar map can be made birational by suitably choosing the middle Bézier control points and their corresponding weights. In contrast to the edges of complex rational maps of degree one, which are circular arcs, the edges of the planar maps can be generalized to hyperbolic and parabolic arcs by invoking the hyperbolic and parabolic numbers.
ATC-YOLOv5: Fruit Appearance Quality Classification Algorithm Based on the Improved YOLOv5 Model for Passion Fruits
Authors: Liu Changhong , Lin Weiren , Feng Yifeng , Guo Ziqing , Xie Zewen
Published: August 2023

Passion fruit, renowned for its significant nutritional, medicinal, and economic value, is extensively cultivated in subtropical regions such as China, India, and Vietnam. In the production and processing industry, the quality grading of passion fruit plays a crucial role in the supply chain. However, the current process relies heavily on manual labor, resulting in inefficiency and high costs, which reflects the importance of expanding the application of fruit appearance quality classification mechanisms based on computer vision. Moreover, the existing passion fruit detection algorithms mainly focus on real-time detection and overlook the quality-classification aspect. This paper proposes the ATC-YOLOv5 model based on deep learning for passion fruit detection and quality classification. First, an improved Asymptotic Feature Pyramid Network (APFN) is utilized as the feature-extraction network, which is the network modified in this study by adding weighted feature concat pathways. This optimization enhances the feature flow between different levels and nodes, allowing for the adaptive and asymptotic fusion of richer feature information related to passion fruit quality. Secondly, the Transformer Cross Stage Partial (TRCSP) layer is constructed based on the introduction of the Multi-Head Self-Attention (MHSA) layer in the Cross Stage Partial (CSP) layer, enabling the network to achieve a better performance in modeling long-range dependencies. In addition, the Coordinate Attention (CA) mechanism is introduced to enhance the network’s learning capacity for both local and non-local information, as well as the fine-grained features of passion fruit. Moreover, to validate the performance of the proposed model, a self-made passion fruit dataset is constructed to classify passion fruit into four quality grades. The original YOLOv5 serves as the baseline model. According to the experimental results, the mean average precision (mAP) of ATC-YOLOv5 reaches 95.36%, and the mean detection time (mDT) is 3.2 ms, which improves the mAP by 4.83% and the detection speed by 11.1%, and the number of parameters is reduced by 10.54% compared to the baseline, maintaining the lightweight characteristics while improving the accuracy. These experimental results validate the high detection efficiency of the proposed model for fruit quality classification, contributing to the realization of intelligent agriculture and fruit industries.
A Survey on Fair Allocation of Chores
Authors: Guo Hao , Li Weidong , Deng Bin
Published: August 2023

Wherever there is group life, there has been a social division of labor and resource allocation, since ancient times. Examples include ant colonies, bee colonies, and wolf colonies. Different roles are responsible for different tasks. The same is true of human beings. Human beings are the largest social group in nature, among whom there are intricate social networks and interest networks between individuals. In such a complex relationship, how do decision makers allocate resources or tasks to individuals in a fair way? This is a topic worthy of further study. In recent decades, fair allocation has been at the core of research in economics, mathematics and other fields. The fair allocation problem is to assign a set of items to a set of agents so that each agent’s allocation is as fair as possible to satisfy each agent. The fairness measurements followed in current research include envy-freeness, proportionality, equitability, maximin share fairness, competitive equilibrium, maximum Nash social diswelfare, and so on. In this paper, the main concern is the allocation of chores. We discuss this problem in two parts: divisible and indivisible. We comprehensively review the existing results, algorithms, and approximations that meet various fairness criteria in chronological order. The relevant results of achieving fairness and efficiency are also discussed. In addition, we propose some open questions and future research directions for this problem based on existing research.
Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM
Authors: Xie Baoshan , Li Fei , Li Hao , Wang Liya , Yang Aimin
Published: August 2023

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status.
Numerical Investigation on Suction Flow Control Technology for a Blunt Trailing Edge Hydrofoil
Authors: Yang Peng , Zhang Chiye , Yan Hongyeyu , Ren Yifan , Ye Changliang , Heng Yaguang , Zheng Yuan
Published: August 2023

The generation of hydro-mechanical resonance is related to the transition of the boundary layer and the development of vortex shedding. The application effect of suction control in hydrodynamics is equally deserving of consideration as an active control technique in aerodynamics. This study examines how suction control affects the flow field of the NACA0009 blunt trailing edge hydrofoil using the γ transition model. Firstly, the accuracy of the numerical method is checked by performing a three-dimensional hydrofoil numerical simulation. Based on this, three-dimensional hydrofoil suction control research is conducted. According to the results, the suction control increases the velocity gradient in the boundary layer and delays the position of transition. The frequency of vortex shedding in the wake region lowers, and the peak value of velocity fluctuation declines. The hydrofoil hydrodynamic performance may be successfully improved with a proper selection of the suction coefficient via research of the suction coefficient and suction position on the flow field around the hydrofoil. The lift/drag ratio goes up as the suction coefficient goes up. The boundary layer displacement thickness and momentum thickness are at their lowest points, and the velocity fluctuation amplitude in the wake region is at its lowest point as the suction coefficient Cμ = 0.003. When the suction slots are at the leading edge, the momentum loss in the boundary layer is minimal and the velocity fluctuation in the wake zone is negligible.
When Critical View of Safety Fails: A Practical Perspective on Difficult Laparoscopic Cholecystectomy
Authors: Alius Catalin , Serban Dragos , Bratu Dan Georgian , Tribus Laura Carina , Vancea Geta , Stoica Paul Lorin , Motofei Ion , Tudor Corneliu , Serboiu Crenguta , Costea Daniel Ovidiu , Serban Bogdan , Dascalu Ana Maria , Tanasescu Ciprian , Geavlete Bogdan , Cristea Bogdan Mihai
Published: August 2023

The incidence of common bile duct injuries following laparoscopic cholecystectomy (LC) remains three times higher than that following open surgery despite numerous attempts to decrease intraoperative incidents by employing better training, superior surgical instruments, imaging techniques, or strategic concepts. This paper is a narrative review which discusses from a contextual point of view the need to standardise the surgical approach in difficult laparoscopic cholecystectomies, the main strategic operative concepts and techniques, complementary visualisation aids for the delineation of anatomical landmarks, and the importance of cognitive maps and algorithms in performing safer LC. Extensive research was carried out in the PubMed, Web of Science, and Elsevier databases using the terms ”difficult cholecystectomy”, ”bile duct injuries”, ”safe cholecystectomy”, and ”laparoscopy in acute cholecystitis”. The key content and findings of this research suggest there is high intersocietal variation in approaching and performing LC, in the use of visualisation aids, and in the application of safety concepts. Limited papers offer guidelines based on robust data and a timid recognition of the human factors and ergonomic concepts in improving the outcomes associated with difficult cholecystectomies. This paper highlights the most relevant recommendations for dealing with difficult laparoscopic cholecystectomies.
Promoting Self-Management and Independent Living of Older Individuals with Chronic Diseases through Technology: A Study of Self-Reported Needs, Priorities, and Preferences
Authors: Kalaitzaki Argyroula , Rovithis Michael , Dimitropoulos Alexios , Koukouli Sofia , Linardakis Manolis , Katsiri Elli , Rikos Nikos , Vasilopoulos George , Tsolas George , Papachristou Aikaterini , Dimitrantzou Anastasia , Katsiris Dimitrios , Stavropoulou Areti
Published: August 2023

Background and Objectives: Older patients’ needs are rarely examined beforehand, and thus, although technology-based tools can enhance self-management, acceptability rates are still low. This study aimed to examine and compare self-reported needs, priorities, and preferences of older patients with heart failure (HF), diabetes mellitus type II (DM2), and chronic obstructive pulmonary disease (COPD) toward technology use to enhance self-management. Materials and Methods: A convenience sample of 473 participants over 60 s (60.5% females), diagnosed with HF (n= 156), DM2 (n = 164), or COPD (n = 153) was recruited. They were administered a questionnaire about the usefulness of technology in general and in specific areas of disease management. Results: Most participants (84.7%) admitted that technology is needed for better disease management. This was equally recognized across the three groups both for the overall and specific areas of disease management (in order of priority: “Information”, “Communication with Physicians and Caregivers”, and “Quality of Life and Wellbeing”). Sociodemographic differences were found. Cell phones and PCs were the devices of preference. The four common features prioritized by all three groups were related to ‘information about disease management’ (i.e., monitoring symptoms, reminders for medication intake, management and prevention of complications), whereas the fifth one was related to ‘communication with physicians and caregivers (i.e., in case of abnormal or critical signs). The top disease-specific feature was also monitoring systems (of respiratory rate or blood sugar or blood pressure, and oxygen), whereas other disease-specific features followed (i.e., maintaining normal weight for HF patients, adjusting insulin dose for DM2 patients, and training on breathing exercises for COPD patients). Conclusions: Older individuals in these three groups seem receptive to technology in disease management. mHealth tools, incorporating both common and disease-specific features and addressing different chronic patients, and also being personalized at the same time, could be cost-saving and useful adjuncts in routine clinical care to improve self-management.
Feasibility and Efficacy of the Newly Developed Robotic Hybrid Assistive Limb Shoulder Exercises in Patients with C5 Palsy during the Acute Postoperative Phase
Authors: Kubota Shigeki , Kadone Hideki , Shimizu Yukiyo , Watanabe Hiroki , Koda Masao , Sankai Yoshiyuki , Yamazaki Masashi
Published: August 2023

Background and Objectives: Although postoperative C5 palsy is a frequent complication of cervical spine surgery, no effective therapeutic rehabilitation approach has been established for postoperative C5 palsy. The purpose of this study was to find evidence confirming the effectiveness and feasibility of robotic Hybrid Assistive Limb (HAL) shoulder exercises for C5 palsy. Materials and Methods: In this before-after, uncontrolled case series clinical study, we performed a mean of 11.7 shoulder training sessions using a shoulder HAL immediately after the onset of C5 palsy in seven shoulders of six patients who developed postoperative C5 palsy and had difficulty raising their shoulder during the acute postoperative phase of cervical spine surgery. Shoulder HAL training was introduced as early as possible after evaluating the general condition of all inpatients who developed C5 palsy. Patients underwent shoulder abduction training using shoulder HAL on an inpatient and outpatient basis at 2-week or 1-month intervals. Adverse events associated with shoulder HAL training were investigated. The shoulder abduction angle and power without the shoulder HAL were evaluated before shoulder HAL usage, at every subsequent session, and upon completion of all sessions. Results: Severe adverse events due to shoulder HAL training were not reported. After completion of all shoulder HAL sessions, all patients showed improved shoulder elevation, while shoulder abduction angle and power improved over time. Conclusions: Shoulder elevation training with HAL in patients in the acute stage of postoperative C5 palsy has the potential to demonstrate improvement in shoulder joint function with a low risk of developing severe adverse events.
Using Single Peroneal Longus Tendon Graft for Segmental Meniscus Transplantation and Revision Anterior Cruciate Ligament Combined Anterolateral Reconstruction
Authors: Yang Ling , Chiu Chih-Hao , Hsu Kuo-Yao , Chuang Chieh-An , Chen Alvin Chao-Yu , Chan Yi-Sheng , Yang Cheng-Pang
Published: August 2023

This case report describes a new approach to segmental meniscal reconstruction using a peroneal longus autograft in a patient with recurrent traumatic medial meniscus tear and anterior cruciate ligament reconstruction (ACLR) failure. While allograft meniscal transplantation is the preferred method for treating meniscal deficiency, its high cost and various legal regulations have limited its widespread use. Autologous tendon grafts have been proposed as a substitute for allograft meniscus transplantation, but their initial results were poor, leading to little progress in this area. However, recent animal experiments and clinical studies have demonstrated promising results in using autologous tendon grafts for meniscal transplantation, including improvements in pain and quality of life for patients. Further research is needed to evaluate the effectiveness of segmental meniscal reconstruction using autologous tendon grafts, but it could potentially lead to more accessible and cost-effective treatment options for patients with meniscal deficiency.
Processing of Alluvial Deposit Sands with a High Content of Copper and Nickel Using Combined Enrichment Technology
Authors: Latyuk Elena , Goryachev Andrey , Makarov Dmitriy
Published: August 2023

The aim of the present research was to examine the process of bioleaching and the application of a combined process for the recovery of copper and nickel from industrial sand deposits. The investigated sample of sands finer than 0.1 mm in size contained 0.32% Ni and 0.22% Cu. Industrial sands were processed by bioleaching in flasks on a thermostatically controlled shaker. In addition, sand roasting experiments were carried out with ammonium sulfate. An attempt was also made to use a combined process, including low-temperature roasting of the sands mixed with ammonium sulfate, water-leaching of the roasted mixture, and subsequent biological after-leaching of the residue. In the process of roasting the industrial sands in a mixture including ammonium sulfate at a temperature of 400 °C, more than 70% of the non-ferrous metals were recovered. We examined the possibility of recovering non-ferrous metals using a combined process including low-temperature roasting of industrial sands and the additional recovery of non-ferrous metals by bioleaching using the Acidithiobacillus ferrivorans bacterial strain, which was found to increase the recovery of non-ferrous metals to up to 90%.
Numerical Investigation of Fatigue Crack Propagation Behaviour of 550E High-Performance Steel
Authors: Xiao Linfa , Lin Heng , Wang Yongxiang , Yang Yiming , Chen Huapeng
Published: August 2023

The fatigue crack propagation behaviour of Q550E high-performance steel (HPS) is studied in this paper. Static tensile testing and fatigue crack propagation testing were carried out, and the results were compared with those of Q235. Finite element models were developed and verified against the experimental results. The impacts of the initial crack angle, crack depth ratio, stress ratio, thickness, and corrosion pitting on the fatigue crack propagation behaviour of the HPS were analysed. The results show that the fatigue life of Q550 was reduced by 18% due to the corrosion pitting, but it did not change the crack propagation path. When the stress intensity factor is higher than a certain value, the fatigue performance of Q235 is better than that of Q550E. The initial crack angle of 52.5° is the critical angle of the crack stress intensity factor. The steel tends to fracture as the crack depth ratio increases, and more attention should be paid to the effective crack length in engineering practice. An increasing stress ratio leads to a smaller stress intensity factor, and the thickness affects the stress intensity factor in the later stage. The crack stress intensity factor around the corrosion pits gradually decreases along the thickness direction, and the crack tips around the corrosion pits tend to reach the yield state initially, accelerating the fatigue fracture of the specimen and ultimately leading to a decrease in fatigue life.
Experimental Determination of the Standard Gibbs Energy of Formation of Fe3–xVxO4 at 1473 K
Authors: Miao Qingdong , Li Ming , Yan Baijun
Published: August 2023

In the present study, an approach of determining the standard Gibbs energy of formation of Fe3–xVxO4 was proposed firstly, then the standard Gibbs energies of formation of a variety of Fe3–xVxO4 were determined experimentally, and finally, a calculating model of the standard Gibbs energy of formation of Fe3–xVxO4 was established. The detailed results are as follows: (1) the standard Gibbs energy of formation of Fe3–xVxO4 can be determined successfully by two steps; the first is to measure the chemical potential of Fe in Fe3–xVxO4 under fixed oxygen partial pressure, the second is to derive the chemical potential of V in Fe3–xVxO4 by Gibbs–Duhem relation; (2) the standard Gibbs energies of formation of Fe3–xVxO4 are mainly decided by the Fe/V molar ratio, and almost not influenced by the oxygen partial pressure in the range from 2.39 × 10−12 to 3.83 × 10−11 atm; (3) in this oxygen partial pressure range, the standard Gibbs energies of formation of Fe3–xVxO4 can be calculated satisfactorily by the following model: ΔfGFe3−xVxO4θJ/mol=1−x/2ΔfGFe3O4θ+x/2ΔfGFeV2O4θ+1−x/2RTln1−x/2+x/2RTlnx/2 − 168627.481−x/2x/2.