bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Efficiency of Antimicrobial Plant Extracts from Artemisia afra and Eucalyptus globulus as Coatings for Textiles
Authors: Nortjie Elvino , Basitere Moses , Moyo Doice , Nyamukamba Pardon
Published: February 2024

This study aimed to assess the antimicrobial activities of plant extracts from Artemisia afra and Eucalyptus globulus when used as coatings for textiles. A pulsed ultrasound-assisted extraction method (PUAE) was employed to obtain methanolic and hexanoic extracts from both plants. Eucalyptus globulus methanol extraction exhibited the highest yield at 22.76% (±0.61%), while Artemisia afra demonstrated lower yields. Phytochemical screening identified various secondary metabolites in the extracts, including phenols, quinones, and steroids. Antimicrobial tests against Staphylococcus aureus and Escherichia coli revealed varying degrees of susceptibility, with Eucalyptus globulus hexanoic extracts showing the highest activity against Staphylococcus aureus at an average percentage growth of 18.74% (±0.26%). Minimum inhibitory concentration (MIC) values were determined for the extracts, but complete inhibition did not occur at concentrations below 500 μg/mL. The extracts exhibited varying effects on Staphylococcus aureus and Escherichia coli growth, with some extracts promoting bacterial growth. Coating textiles with Eucalyptus globulus methanolic extracts demonstrated antibacterial activity against Staphylococcus aureus with the highest zone of inhibition observed in cotton-coated samples (258.4 mm2). Polyester-coated samples exhibited smaller inhibition zones, with the lowest observed in Eucalyptus globulus methanolic extract coating (65.97 mm2). Scanning electron microscope (SEM) analysis revealed visible surface morphology changes in coated fabrics, depicting fine, cluster, lumpy, flaky, and fragment-like morphologies. Laundering effects on coated fabrics were investigated, showing a significant decrease in antimicrobial activity after washing. Fourier-transform infrared spectroscopy (FTIR) identified functional groups in the extracts associated with antimicrobial properties. The complexity of the bioactive compounds suggests potential antimicrobial efficacy, resting on factors such as geographical location, climate, and extraction methods. Notwithstanding the limitations, this study contributes valuable insights into the use of plant extracts as antimicrobial coatings for textiles.
The Invasive Weed Trianthema portulacastrum in Israel
Authors: Goldwasser Yaakov , Rabinowitz Onn , Achdary Guy , Kapiluto Omer , Abu-Nasser Jackline , Smirnov Evgeny , Eizenberg Hanan
Published: February 2024

Trianthema portulacastrum L. (Aizoaceae), commonly known as desert horse purslane or black pigweed, is a C4 dicot succulent annual herb that is widespread in Southeast Asia, tropical America, Africa, and Australia. In Israel, it is an invasive weed of increasing importance in agricultural fields. The aim of this study was to investigate the biology of this invasive weed and its spread in the Hula Valley of Israel. Initial studies included the investigation of the T. portulacastrum specimens held at the Israel National Herbarium. On-site surveillance for the identification of weed infestation locations was conducted in the Hula Valley throughout 2019–2022, and an infestation map was assembled. In a study of the plant biology, greenhouse pot experiments revealed that T. portulacastrum seeds emerge best from the upper soil levels, and as seed depth increases, the emergence rate decreases, so that at 6 cm soil depth, there was no emergence. In controlled-environment growth chamber studies, there were no significant differences in germination with or without light. A maximum germination of 81% was observed for a 12 h night/day of 25/35 °C regime. Germination rates decreased with the decrease in temperature. A seed germination thermal time model that was developed for estimating the minimum temperature required for germination (Tbase) computed this temperature to be 10 °C. This study revealed the biology, in particular seed germination and emergence requirements, of the invasive weed T. portulacastrum that has spread in the Hula Valley in Israel and beyond. Future research will focus on an examination of control measures to combat this invasive weed.
Spatial Variation in Responses of Plant Spring Phenology to Climate Warming in Grasslands of Inner Mongolia: Drivers and Application
Authors: Lu Guang , Fang Mengchao , Zhang Shuping
Published: February 2024

Plant spring phenology in grasslands distributed in the Northern Hemisphere is highly responsive to climate warming. The growth of plants is intricately influenced by not only air temperature but also precipitation and soil factors, both of which exhibit spatial variation. Given the critical impact of the plant growth season on the livelihood of husbandry communities in grasslands, it becomes imperative to comprehend regional-scale spatial variation in the response of plant spring phenology to climate warming and the effects of precipitation and soil factors on such variation. This understanding is beneficial for region-specific phenology predictions in husbandry communities. In this study, we analyzed the spatial pattern of the correlation coefficient between the start date of the plant growth season (SOS) and the average winter–spring air temperature (WST) of Inner Mongolia grassland from 2003 to 2019. Subsequently, we analyzed the importance of 13 precipitation and soil factors for the correlation between SOS and average WST using a random forest model and analyzed the interactive effect of the important factors on the SOS using linear mixing models (LMMs). Based on these, we established SOS models using data from pastoral areas within different types of grassland. The percentage of areas with a negative correlation between SOS and average WST in meadow and typical grasslands was higher than that in desert grasslands. Results from the random forest model highlighted the significance of snow cover days (SCD), soil organic carbon (SOC), and soil nitrogen content (SNC) as influential factors affecting the correlation between SOS and average WST. Meadow grasslands exhibited significantly higher levels of SCD, SOC, and SNC compared to typical and desert grasslands. The LMMs indicated that the interaction of grassland type and the average WST and SCD can effectively explain the variation in SOS. The multiple linear models that incorporated both average WST and SCD proved to be better than models utilizing WST or SCD alone in predicting SOS. These findings indicate that the spatial patterns of precipitation and soil factors are closely associated with the spatial variation in the response of SOS to climate warming in Inner Mongolia grassland. Moreover, the average WST and SCD, when considered jointly, can be used to predict plant spring phenology in husbandry communities.
Innovative Carbon Ball Frameworks: Elevating Energy Storage Performance and Enhancing CO2 Capture Efficiency
Authors: Periyasamy Thirukumaran , Asrafali Shakila Parveen , Kim Seong-Cheol , Lee Jaewoong
Published: February 2024

A novel porous carbon, derived from polybenzoxazine and subjected to hydrogen peroxide treatment, has been meticulously crafted to serve dual functions as a supercapacitor and a CO2 capture material. While supercapacitors offer a promising avenue for electrochemical energy storage, their widespread application is hampered by relatively low energy density. Addressing this limitation, our innovative approach introduces a three-dimensional holey carbon ball framework boasting a hierarchical porous structure, thereby elevating its performance as a metal-free supercapacitor electrode. The key to its superior performance lies in the intricate design, featuring a substantial ion-accessible surface area, well-established electron and ion transport pathways, and a remarkable packing density. This unique configuration endows the holey carbon ball framework electrode with an impressive capacitance of 274 F g−1. Notably, the electrode exhibits outstanding rate capability and remarkable longevity, maintaining a capacitance retention of 82% even after undergoing 5000 cycles in an aqueous electrolyte. Beyond its prowess as a supercapacitor, the hydrogen peroxide-treated porous carbon component reveals an additional facet, showcasing an exceptional CO2 adsorption capacity. At temperatures of 0 and 25 °C, the carbon material displays a CO2 adsorption capacity of 4.4 and 4.2 mmol/g, respectively, corresponding to equilibrium pressures of 1 bar. This dual functionality renders the porous carbon material a versatile and efficient candidate for addressing the energy storage and environmental challenges of our time.
Research on Imbalanced Data Regression Based on Confrontation
Authors: Liu Xiaowen , Tian Huixin
Published: February 2024

The regression model has higher requirements for the quality and balance of data to ensure the accuracy of predictions. However, there is a common problem of imbalanced distribution in real datasets, which directly affects the prediction accuracy of regression models. In order to solve the problem of data imbalance regression, considering the continuity of the target value and the correlation of the data and using the idea of optimization and confrontation, we propose an IRGAN (imbalanced regression generative adversarial network) algorithm. Considering the context information of the target data and the disappearance of the deep network gradient, we constructed a generation module and designed a composite loss function. In the early stages of training, the gap between the generated samples and the real samples is large, which easily causes the problem of non-convergence. A correction module is designed to train the internal relationship between the state and action as well as the subsequent state and reward of the real samples, guide the generation module to generate samples, and alleviate the non-convergence of the training process. The corrected samples and real samples are input into the discriminant module. On this basis, the confrontation idea is used to generate high-quality samples to balance the original samples. The proposed method is tested in the fields of aerospace, biology, physics, and chemistry. The similarity between the generated samples and the real samples is comprehensively measured from multiple perspectives to evaluate the quality of the generated samples, which proves the superiority of the generated module. Regression prediction is performed on the balanced samples processed by the IRGAN algorithm, and it is proven that the proposed algorithm can improve the prediction accuracy in terms of the imbalanced data regression problem.
Modeling Method for Overheated Zone and Two-Phase Zone of Dry Shell-and-Tube Evaporator in Ship Air Conditioning
Authors: He Zhibin , Zhang Qi , Wei Zhenghao , Liao Xingzhe , Wu Xiaoyu , Zhang Jundong , Tan Yanghui
Published: February 2024

This paper researches the heat transfer equation and thermal balance equation of a shell-and-tube evaporator; constructs an accurate mathematical model for the evaporator; and derives equations including detailed and accurate calculation methods for all heat transfer coefficients, such as the refrigerant side heat transfer coefficient, water side heat transfer coefficient, refrigerant kinematic viscosity, density, and specific enthalpy. Adopting this approach involves fitting the relationships between the density, thermal conductivity, kinematic viscosity, and enthalpy of R134a refrigerants in saturated vapor and liquid states. The relationships between superheated gas enthalpy, density, and temperature were also assessed, and heat transfer coefficients were obtained through calculation methods and microelement heat transfer relationships in both the single-phase and two-phase zones, matching empirical formulas concerning the relationship between superheated enthalpy and temperature. Notably, the research utilizes the Simulink approach without relying on M files and S functions to establish the evaporator’s two-phase and superheated zones, as well as an overall simulation model which provides intuitive internal coupling relationships and the coefficient calculation process in the formulas and uses the function “Algebraic Constraint” instead of “memory” or “1/z” to solve algebraic loops, thereby avoiding computation deviations introduced by delays and iterations. Finally, simulation calculations were conducted, and an experimental platform was designed and built for experimental verification which can validate the derived mathematical models. The simulation results, including the evaporator pressure, and chilled water outlet temperature with variation in chilled water mass flow rate, closely matched the experimental outcomes. The simulation model is concise and intuitive. Modifying parameters such as the thermal conductivity of the model material is straightforward, thereby alleviating the workload for researchers. It also facilitates an understanding of model principles for beginners. Moreover, the database generated from the model allows for fault analysis, diagnosis, and decision evaluation.
Numerical Simulation Study on the Damage Mechanism of the Combined Perforating Well Testing Tubing in Ultra-Deep Wells
Authors: Jiang Jiadong , Deng Qiao , Yang Dong , Qi Guilin , Zhang Fan , Tan Leichuan
Published: February 2024

During perforation in ultra-deep wells, the blast shock wave can induce dynamic responses of the perforating tubing, leading to potential downhole accidents such as vibration, deformation, and even fracture of the perforating tubing. To comprehend the dynamic response characteristics of the perforating tubing under blast impact load, we conducted a joint finite element simulation using SolidWorks, Hypermesh, and LS-DYNA. The simulation included deformation analysis, motion analysis, and strength analysis of the perforating tubing. By analyzing these factors, we obtained the change in velocity, acceleration, and equivalent stress of the perforating tubing over time under the blast load. The finite element analysis indicates the following: (a) the bottom of the perforating tubing is susceptible to significant tension compression cycle; (b) the velocity amplitude variation is smallest at the top of the perforating tubing, while the frequency and peak values of velocity changes are maximal at the bottom of the perforating tubing; and (c) the top and bottom of the tubing string are the vulnerable parts of the perforating tubing system.
Multi-Decadal Nutrient Management and Trends in Two Catchments of Lake Okeechobee
Authors: Cooper Rachael Z. , Ergas Sarina J. , Nachabe Mahmood
Published: February 2024

Despite years of efforts to improve water quality, harmful algal blooms remain a chronic phenomenon, with devastating environmental, economic, and social impacts in many regions worldwide. In this study, we assessed the complexity of nutrient pollution attributed to harmful algal blooms in South Florida (USA) by analyzing 20 years of flow and nutrient data within two headwater basins in the Lake Okeechobee (LO) watershed. The study used an established advanced regression method, the Weighted Regression on Time, Discharge, and Season (WRTDS) method, as an analysis framework to examine the impact of nutrient management practices on water quality trends. The WRTDS method produced total phosphorus (TP) and total nitrogen (TN) concentration and flux trends, which were then compared with existing and historic nutrient management records within the basin. Results from this study highlight divergences in progress to improve water quality. Nutrient management practices only had a weak impact on TP and TN flux trends in one of the two basins, where TP flux decreased 2% per year, and TN flux decreased 0.1% per year. TP and TN flux increased in the second basin. Variances of improvement between the two basins are likely attributable to differences in contemporary point source loading and legacy nutrient pools from non-point source inputs 20 years or more before the analysis period. The long-lasting impacts of legacy nutrients also emphasize a need for investments in technologies and practices that can withdraw nutrients from enriched soil and water.
Evaluation of Synthetic-Temporal Imagery as an Environmental Covariate for Digital Soil Mapping: A Case Study in Soils under Tropical Pastures
Authors: Avalos Fabio Arnaldo Pomar , de Menezes Michele Duarte , Acerbi Júnior Fausto Weimar , Curi Nilton , Avanzi Junior Cesar , Silva Marx Leandro Naves
Published: February 2024

Digital soil maps are paramount for supporting environmental process analysis, planning for the conservation of ecosystems, and sustainable agriculture. The availability of dense time series of surface reflectance data provides valuable information for digital soil mapping (DSM). A detailed soil survey, along with a stack of Landsat 8 SR data and a rainfall time series, were analyzed to evaluate the influence of soil on the temporal patterns of vegetation greenness, assessed using the normalized difference vegetation index (NDVI). Based on these relationships, imagery depicting land surface phenology (LSP) metrics and other soil-forming factors proxies were evaluated as environmental covariates for DSM. The random forest algorithm was applied as a predictive model to relate soils and environmental covariates. The study focused on four soils typical of tropical conditions under pasture cover. Soil parent material and topography covariates were found to be similarly important to LSP metrics, especially those LSP images related to the seasonal availability of water to plants, registering significant contributions to the random forest model. Stronger effects of rainfall seasonality on LSP were observed for the Red Latosol (Ferralsol). The results of this study demonstrate that the addition of temporal variability of vegetation greenness can be used to assess soil subsurface processes and assist in DSM.
A Space Target Detection Method Based on Spatial–Temporal Local Registration in Complicated Backgrounds
Authors: Su Yueqi , Chen Xin , Cang Chen , Li Fenghong , Rao Peng
Published: February 2024

Human space exploration has brought a growing crowded operating environment for in-orbit spacecraft. Monitoring the space environment and detecting space targets with photoelectric equipment has extensive and realistic significance in space safety. In this study, a local spatial–temporal registration (LSTR) method is proposed to detect moving small targets in space. Firstly, we applied the local region registration to estimate the neighbor background motion model. Secondly, we analyzed the temporal local grayscale difference between the strong clutter and target region and measured the temporal local–central region difference to enhance the target. Then, the temporal pixel contrast map was calculated, which further retains the target signal and suppresses the residue clutter. Finally, a simple adaptive threshold segmentation algorithm was applied to the saliency map to segment the targets. Comparative experiments were conducted on four groups of image sequences to validate the efficiency and robustness of the algorithm. The experimental findings indicate that the proposed method performs well in target enhancement and clutter suppression under different scenarios.
Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging
Authors: Vivoli Emanuele , Bertini Marco , Capineri Lorenzo
Published: February 2024

This paper presents a pioneering study in the application of real-time surface landmine detection using a combination of robotics and deep learning. We introduce a novel system integrated within a demining robot, capable of detecting landmines in real time with high recall. Utilizing YOLOv8 models, we leverage both optical imaging and artificial intelligence to identify two common types of surface landmines: PFM-1 (butterfly) and PMA-2 (starfish with tripwire). Our system runs at 2 FPS on a mobile device missing at most 1.6% of targets. It demonstrates significant advancements in operational speed and autonomy, surpassing conventional methods while being compatible with other approaches like UAV. In addition to the proposed system, we release two datasets with remarkable differences in landmine and background colors, built to train and test the model performances.
Evaluation of ICESat-2 Significant Wave Height Data with Buoy Observations in the Great Lakes and Application in Examination of Wave Model Predictions
Authors: Li Linfeng , Fujisaki-Manome Ayumi , Miller Russ , Titze Dan , Henderson Hayden
Published: February 2024

High waves and surges associated with storms pose threats to the coastal communities around the Great Lakes. Numerical wave models, such as WAVEWATCHIII, are commonly used to predict the wave height and direction for the Great Lakes. These predictions help determine risks and threats associated with storm events. To verify the reliability and accuracy of the wave model outputs, it is essential to compare them with observed wave conditions (e.g., significant wave height), many of which come from buoys. However, in the Great Lakes, most of the buoys are retrieved before those lakes are frozen; therefore, winter wave measurements remain a gap in the Great Lakes’ data. To fill the data gap, we utilize data from the Inland Water Surface Height product of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as complements. In this study, the data quality of ICESat-2 is evaluated by comparing with wave conditions from buoy observations in the Great Lakes. Then, we evaluate the model quality of NOAA’s Great Lakes Waves-Unstructured Forecast System version 2.0 (GLWUv2) by comparing its retrospective forecast simulations for significant wave height with the significant wave height data from ICESat-2, as well as data from a drifting Spotter buoy that was experimentally deployed in the Great Lakes. The study indicates that the wave measurements obtained from ICESat-2 align closely with the in situ buoy observations, displaying a root-mean-square error (RMSE) of 0.191 m, a scatter index (SI) of 0.46, and a correlation coefficient of 0.890. Further evaluation suggests that the GLWUv2 tends to overestimate the wave conditions in high wave events during winter. The statistics show that the RMSE in 0–0.8 m waves is 0.257 m, while the RMSE in waves higher than 1.5 m is 0.899 m.
FFT-Based Simultaneous Calculations of Very Long Signal Multi-Resolution Spectra for Ultra-Wideband Digital Radio Frequency Receiver and Other Digital Sensor Applications
Authors: Wu Chen , Low Michael
Published: February 2024

The discrete Fourier transform (DFT) is the most commonly used signal processing method in modern digital sensor design for signal study and analysis. It is often implemented in hardware, such as a field programmable gate array (FPGA), using the fast Fourier transform (FFT) algorithm. The frequency resolution (i.e., frequency bin size) is determined by the number of time samples used in the DFT, when the digital sensor’s bandwidth is fixed. One can vary the sensitivity of a radio frequency receiver by changing the number of time samples used in the DFT. As the number of samples increases, the frequency bin width decreases, and the digital receiver sensitivity increases. In some applications, it is useful to compute an ensemble of FFT lengths; e.g., 2P−j for j=0, 1, 2, …, J, where j is defined as the spectrum level with frequency resolution  2j·Δf. Here Δf is the frequency resolution at j=0. However, calculating all of these spectra one by one using the conventional FFT method would be prohibitively time-consuming, even on a modern FPGA. This is especially true for large values of P; e.g., P≥20. The goal of this communication is to introduce a new method that can produce multi-resolution spectrum lines corresponding to sample lengths  2P−j for all J+1 levels, concurrently, while one long 2P-length FFT is being calculated. That is, the lower resolution spectra are generated naturally as by-products during the computation of the 2P-length FFT, so there is no need to perform additional calculations in order to obtain them.
Optimization of Internet of Things Remote Desktop Protocol for Low-Bandwidth Environments Using Convolutional Neural Networks
Authors: Wang Hejun , Deng Kai , Zhong Guoxin , Duan Yubing , Yin Mingyong , Meng Fanzhi , Wang Yulong
Published: February 2024

This paper discusses optimizing desktop image quality and bandwidth consumption in remote IoT GUI desktop scenarios. Remote desktop tools, which are crucial for work efficiency, typically employ image compression techniques to manage bandwidth. Although JPEG is widely used for its efficiency in eliminating redundancy, it can introduce quality loss with increased compression. Recently, deep learning-based compression techniques have emerged, challenging traditional methods like JPEG. This study introduces an optimized RFB (Remote Frame Buffer) protocol based on a convolutional neural network (CNN) image compression algorithm, focusing on human visual perception in desktop image processing. The improved RFB protocol proposed in this paper, compared to the unoptimized RFB protocol, can save 30–80% of bandwidth consumption and enhances remote desktop image quality, as evidenced by improved PSNR and MS-SSIM values between the remote desktop image and the original image, thus providing superior desktop image transmission quality.
An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network
Authors: Zaarour Nour , Hakem Nadir , Kandil Nahi
Published: February 2024

Sensor localization remains a crucial function within the context of wireless sensor networks (WSNs) and is a delicate concern that has attracted many researchers’ attention. Undoubtedly, a good distance estimation between different wireless sensors allows us to estimate their accurate locations in the network well. In this article, we present a simple but very effective anchor-free localization scheme for wireless sensor networks called the contextual received signal strength approach (CRSSA) localization scheme. We use the received signal strength (RSS) values and the contextual network connectivity within an anchor-free WSN. We present and thoroughly analyze a novel joint estimation methodology for determining the range, path loss exponent (PLE), and inter-node distances in a composite fading model that addresses small-scale multipath fading and large-scale path loss shadowing effects. We formulate analytical expressions for key parameters, the node’s communication range and the PLE value, as functions of the sensor’s number, the network’s connectivity, and the network density. Once these parameters are estimated, we estimate the inter-node distances and the positions of nodes, with relatively high accuracy, based on the assumed propagation model in a two-dimensional anchor-free WSN. The effectiveness of the CRSSA is evaluated through extensive simulations assuring its estimation accuracy in anchor-free localization.
A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks
Authors: Sharma Aakanksha , Balasubramanian Venki , Kamruzzaman Joarder
Published: February 2024

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward–punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach—tDQN—effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss.
Thermal-Adaptation-Behavior-Based Thermal Sensation Evaluation Model with Surveillance Cameras
Authors: Wang Yu , Duan Wenjun , Li Junqing , Shen Dongdong , Duan Peiyong
Published: February 2024

The construction sector is responsible for almost 30% of the world’s total energy consumption, with a significant portion of this energy being used by heating, ventilation and air-conditioning (HVAC) systems to ensure people’s thermal comfort. In practical applications, the conventional approach to HVAC management in buildings typically involves the manual control of temperature setpoints by facility operators. Nevertheless, the implementation of real-time alterations that are based on the thermal comfort levels of humans inside a building has the potential to dramatically improve the energy efficiency of the structure. Therefore, we propose a model for non-intrusive, dynamic inference of occupant thermal comfort based on building indoor surveillance camera data. It is based on a two-stream transformer-augmented adaptive graph convolutional network to identify people’s heat-related adaptive behaviors. The transformer specifically strengthens the original adaptive graph convolution network module, resulting in further improvement to the accuracy of the detection of thermal adaptation behavior. The experiment is conducted on a dataset including 16 distinct temperature adaption behaviors. The findings indicate that the suggested strategy significantly improves the behavior recognition accuracy of the proposed model to 96.56%. The proposed model provides the possibility to realize energy savings and emission reductions in intelligent buildings and dynamic decision making in energy management systems.
A Model of Multi-Finger Coordination in Keystroke Movement
Authors: Lin Jialuo , Ding Baihui , Song Zilong , Li Zheng , Li Shengchao
Published: February 2024

In multi-finger coordinated keystroke actions by professional pianists, movements are precisely regulated by multiple motor neural centers, exhibiting a certain degree of coordination in finger motions. This coordination enhances the flexibility and efficiency of professional pianists’ keystrokes. Research on the coordination of keystrokes in professional pianists is of great significance for guiding the movements of piano beginners and the motion planning of exoskeleton robots, among other fields. Currently, research on the coordination of multi-finger piano keystroke actions is still in its infancy. Scholars primarily focus on phenomenological analysis and theoretical description, which lack accurate and practical modeling methods. Considering that the tendon of the ring finger is closely connected to adjacent fingers, resulting in limited flexibility in its movement, this study concentrates on coordinated keystrokes involving the middle and ring fingers. A motion measurement platform is constructed, and Leap Motion is used to collect data from 12 professional pianists. A universal model applicable to multiple individuals for multi-finger coordination in keystroke actions based on the backpropagation (BP) neural network is proposed, which is optimized using a genetic algorithm (GA) and a sparrow search algorithm (SSA). The angular rotation of the ring finger’s MCP joint is selected as the model output, while the individual difference information and the angular data of the middle finger’s MCP joint serve as inputs. The individual difference information used in this study includes ring finger length, middle finger length, and years of piano training. The results indicate that the proposed SSA-BP neural network-based model demonstrates superior predictive accuracy, with a root mean square error of 4.8328°. Based on this model, the keystroke motion of the ring finger’s MCP joint can be accurately predicted from the middle finger’s keystroke motion information, offering an evaluative method and scientific guidance for the training of multi-finger coordinated keystrokes in piano learners.
Changes in the Activity of the Erector Spinae and Gluteus Medius Muscles with the Presence of Simulated Lower Limb Dysmetria
Authors: Benito de Pedro María , Benito de Pedro Ana Isabel , Aguilera Rubio Ángela , Maté Muñoz Jose Luis , Hernández Lougedo Juan
Published: February 2024

(1) Background: Leg length discrepancy (LLD), regardless of its origin, is a very common pathology that can contribute to low back pain. Various authors have pointed out its relationship with the lack of activation of both the gluteus medius (GM) and the ipsilateral erector spinae (ES). The purpose of this study was to identify the activation of the ES and GM with different simulated LLDs, correlating this activation with LBP. In turn, we evaluated whether ES and GM activity has an effect on jumping ability using a CMJ test. (2) Method: A sample of healthy subjects was selected to whom an artificial LLD was applied using 0.5, 1, and 1.5 cm insoles. These three heights were measured using EMG while the subjects walked and performed a counter movement jump (CMJ). The measurements of the insole heights were carried out in random order using a Latin square. Muscle activation patterns were recorded for 30 s at each of the insole heights while the patients walked at 5.7 km/h and they were compared with the maximum voluntary contraction (MVC), both on the ipsilateral and contralateral sides. These muscles were then measured under the same circumstances during the performance of the CMJ. (3) Results: We found statistically significant differences in the flight heights in both the CMJ and DJ. In the comparison, significant differences were found in the flight heights of the CMJ and the DJ using the 5 mm insoles, and in the case of the DJ, also without insoles, with respect to the MVC. We found statistically significant differences in the activation of the GM with the differences in insoles, but not in the activation of the Es in relation to the different insole heights. (4) Conclusions: Insoles of different heights caused activation differences in the medius on the side where the insoles were placed. We can relate this difference in activation to LBP. In relation to the ES, no significant differences were found in the activation of the ipsilateral side of the insole.
Triple Fused Supernumerary Kidneys with Five Pelvicalyceal Systems
Authors: Kumar Naveen , Sharma Srishti , Rizwi Kashif
Published: February 2024

We present a case of supernumerary kidney with fusion of all three kidney units with five pelvicalyceal system (bilateral bifid PCS and PCS of supernumerary kidney). This is probably the first such case reported in literature.
A 10-Year Multicentre Experience of Australian Penile Fracture Repair Outcomes
Authors: Yii Matthew , Gan Chrisdan , Qin Kirby R. , Bolton Damien , Blecher Gideon
Published: February 2024

Objectives and Methods: Penile fractures are a urologic emergency involving the rupture of the tunica albuginea of the corpora cavernosum. Geography is known to impact fracture aetiology, and higher impact aetiology is thought to predispose patients to complex trauma. To review long-term urinary and sexual outcomes following fracture repair in Australia, a retrospective analysis of data from three metropolitan hospital services over 10 years was performed. Only patients with intraoperatively confirmed fractures were included. Patients were contacted by clinicians to complete a survey, which utilised validated questionnaires, including the International Index of Erectile Function-5 (IIEF-5) and the International Prostate Symptom Score (IPSS). Perioperative data were analysed against survey responses. Results: We identified 55 confirmed penile fractures. Twenty-one patients completed questionnaire follow-ups, comprising the study cohort. The median age at the time of the injury was 44.8 years (range: 25–65). The median time from injury to questionnaire completion was 4.0 years (range: 1–10). Furthermore, 95.5% (20) of the injuries occurred during sexual intercourse. The median IIEF-5 score was 23 (range: 5–25); the median IPSS score was 5 (range: 0–22). Seven patients (33.3%) sustained a bilateral cavernosal injury, and eight (38.1%) sustained a urethral injury. Upon conducting a Mann–Whitney U test, no significant relationships were found between bilateral cavernosal injury and IIEF-5 scores (p = 0.7377) or urethral injury and IPSS scores (p = 0.5338). Conclusions: The Australian aetiology of penile fractures appears consistent with that of other Western countries, with subsequent high rates of bilateral cavernosal and urethral injuries. The long-term erectile and urinary function outcomes observed are promising. A larger prospective study would further illuminate the relationship between injury factors and outcomes, revealing information not presented herein due to study limitations relating to the cohort size and follow-up rates.
Transfusion in Radical Cystectomy Increases Overall Morbidity and Mortality: A Retrospective Study Using Data from the American College of Surgeons—National Surgical Quality Improvement Program
Authors: Ayoub Christian H. , Abou Heidar Nassib F. , Armache Alexandre K. , Abou Chawareb Elia , El Hajj Albert
Published: February 2024

Background: Radical cystectomy is a complex procedure imposing significant post-operation complications. Objective: Explore the impact of peri-operative pRBC transfusion on mortality and overall morbidity in a matched cohort. Methods: The American College of Surgeons—National Surgical Quality Improvement Program’s (ACS-NSQIP) dataset was used to select patients who underwent RC in 2008–2019. Patients who witnessed pre-operative transfusion and emergency cases were excluded. Peri-operative pRBC transfusion was defined as an intra-operative or up to 24-h post-operative pRBC transfusion. We matched patients who underwent peri-operative pRBC transfusion to patients who did not receive transfusion. Length of stay, mortality, and overall morbidity were compared between the two matched cohorts. Results: The match cohort was matched on all pre-operative demographics and medical history variables and yielded 3578 matched patients. Patients who underwent peri-operative pRBC transfusion had a longer length of hospital stay (9.3 days) as compared to patients who did not undergo transfusion (8.13 days) (p < 0.001). Furthermore, patients who underwent transfusion also had higher odds of mortality (OR = 1.934) and overall morbidity (OR = 1.443) (p < 0.03). Specifically, patients who underwent transfusion had higher odds of organ space SSI, pneumonia, unplanned intubation, pulmonary embolism, failure to wean off of ventilator, renal insufficiency, urinary tract infections, stroke, myocardial infarction, cardiac arrest requiring CPR, deep vein thrombosis, and septic shock (p < 0.047). Conclusion: Peri-operative pRBC transfusion in RC was associated with longer hospital stays, significant morbidity, and mortality. For this reason, pre-operative patient optimization and possible alternatives to common pRBC practices should be considered in RC to circumvent complications.
The “Clockwork” Model for Deployment Technology Innovations in Sports Industry Ecosystem: Holistic Approach
Authors: Glebova Ekaterina , Desbordes Michel , Czegledi Orsolya
Published: February 2024

The use of technology in different aspects of the sports industry is widespread across the world, affecting sports training, performance, judging, and spectating. However, the integration, deployment, and evolution of technologies in the sports industry ecosystem are still unclear and unexplained. In this paper, we aim to build and explain the conceptual model for deployment technologies in the sports ecosystem in a holistic approach. This conceptual model is based on a literature review and theoretical synthesis, coupled with 15 qualitative unstructured interviews with high-profile sport and technology experts. Then, we formulated 4 hypotheses and confirmed them using 15 qualitative unstructured interviews with technology and sports experts. The in-depth analysis of the literature and collected data let us build the “Clockwork” Model. To better visualize and explain the development of the model of deployment technologies in the sports ecosystem, based on the analysis of theoretical and empirical data, we compare the mechanism of the model with clockwork. Technology deployment is a complicated operational process and involves the continuous sequence of consecutive elements (stages), ideally functioning as a mechanism. Together, the hypotheses underscore the symbiotic relationship between traditional sports infrastructure and technological advancements, highlighting the importance of a balanced and well-functioning ecosystem for overall success and development in the sports industry. All four hypotheses were confirmed during the second set of interviews (N = 15). Furthermore, their synthesis brought us to build and refine the “Clockwork” conceptual model, which explains, articulates, and visually demonstrates the process of how technology innovations appear and evolve in the sports ecosystem; in other words, the continuous and cyclic process of technology implementation and deployment.
Parental Status Connection and Social Network Variety in Adulthood
Authors: Gemar Adam
Published: February 2024

This study enriches the literature on social networks and social capital by investigating how parental status potentially impacts social network diversity in adulthood. Using the 2018 iteration of the General Social Survey (GSS, n = 2348), a high quality nationally representative survey of the United States, we utilize latent class and regression analyses, finding that parental status, especially medium and cross-status occupational connections contribute to social capital in the form of network diversity. Yet, personal socio-economic factors, notably income and race, largely offset parental effects. This underscores the complexity of network composition, emphasizing the influential role of individual resources, attributes, and mobility in shaping social networks and forming bridging social capital.
The Relationship between Dynamic Balance, Jumping Ability, and Agility with 100 m Sprinting Performance in Athletes with Intellectual Disabilities
Authors: Jouira Ghada , Alexe Dan Iulian , Tohănean Dragoș Ioan , Alexe Cristina Ioana , Tomozei Răzvan Andrei , Sahli Sonia
Published: February 2024

Sprinting is a competitive event in athletics that requires a combination of speed, power, agility, and balance. This study investigated the relationship between dynamic balance, jumping ability, and agility with 100 m sprinting performance in athletes with intellectual disabilities, addressing an underexplored connection. A sample of 27 sprinters with intellectual disabilities participated in this study and completed 100 m sprint and various tests, including the Y Balance Test (YBT), the Crossover hop test, squat jump (SJ), countermovement jump (CMJ), and t-test to evaluate their dynamic balance, jumping ability, and agility, respectively. The findings revealed significant negative correlations between the YBT, Crossover hop test, SJ, and CMJ and 100 m sprint performance (r range: −0.41 to −0.79, p < 0.05). Regression analysis identified these variables as significant predictors (R2 = 0.69; p < 0.01). SJ exhibited the strongest association with 100 m sprint performance, (R2 = 0.62, p < 0.01). The agility t-test did not show a significant association. The combination of the YBT ANT and SJ demonstrated a predictive capability for 100 m sprint performance (R2 = 0.67, p < 0.001). In conclusion, this study revealed predictive capabilities between dynamic balance, jumping ability, and 100 m sprint performance in sprinters with intellectual disabilities.
Framework for a Simulation Learning Tool to Optimize Green Star Buildings in South Africa
Authors: Pillay Theogan Logan , Saha Akshay Kumar
Published: February 2024

The Green Building Council of South Africa specifies nine parameters for energy efficiency in buildings. These parameters are in dynamic systemic interaction with each other and with other building design elements. Therefore, the issue of optimization in terms of the Green Star rating system is a complex problem that defies complete resolution and sustainability. Partial resolution, using algorithmic optimization convergence and simulation techniques, holds potential. The specific problem that this paper confronts is the need for engineers, and others, to be able to assess energy-efficient early design decisions within tight time frames. A proposition is made regarding further developing a “green” simulation learning tool for practitioners. This paper explores the potential of MATLAB and EnergyPlus to create a simulated learning space for green energy optimization. While recognized as being an abstraction from the total set of nine Green Building Council of South Africa parameters, the purpose is to introduce principles that can be extended into a multi-variable, more complex context of multiple sustainability criteria. This paper concludes with a framework for a simulation model that optimizes one of the Green Star criteria of the Green Building Council of South Africa supported by case study data for four, five, and six star rated buildings.
Comprehensive Framework for Analysing the Intensity of Land Use and Land Cover Change in Continental Ecuadorian Biosphere Reserves
Authors: Urgilez-Clavijo Andrea , Rivas-Tabares David , Gobin Anne , de la Riva Juan
Published: February 2024

The conventional methods of land use and land cover (LULC) analysis are frequently based on crosstabulation matrices. However, this analysis becomes complex when including sites with multiple management zones and categories at different time points. This is challenging regarding data processing and the presentation of numerous results. We transformed the graphical representation of conventional Intensity Analysis to assess and compare the intensity and magnitude of LULC changes in six Continental Ecuadorian Biosphere Reserves at three levels: interval, category, and transition, and at three time intervals. A dimension reduction strategy was used to convert multiple bar charts into three composite heat maps. The results confirm the global conservation effectiveness in the core zones among the biosphere reserves with less than 10% change, but worrisome dynamics were detected in buffers and transitions with a change of up to 10% for all periods. Deforestation and agriculture were detected as the most relevant land processes. This work highlights the dimension reduction by summarizing 17, 51, and 312 bar charts from conventional Intensity Analysis in three composite heat maps, one for each level of analysis. Systematic suspicious transitions occurred in the water body category because of its dynamics and misclassification in LULC maps.