bluuebunny/crossref_metadata_2025 huggingface dataset:
Calcium Imaging Characterize the Neurobiological Effect of Terahertz Radiation in Zebrafish Larvae
Authors: Song Xin , Li Haibin , Liu Xiuyun , Pang Meijun , Wang Yuye
Published: September 2023

(1) Objective: To explore the neurobiological effects of terahertz (THz) radiation on zebrafish larvae using calcium (Ca2+) imaging technology. (2) Methods: Zebrafish larvae at 7 days post fertilization (dpf) were exposed to THz radiation for 10 or 20 min; the frequency was 2.52 THz and the amplitude 50 mW/cm2. The behavioral experiments, neural Ca2+ imaging, and quantitative polymerase chain reaction (qPCR) of the dopamine-related genes were conducted following the irradiation. (3) Results: Compared with the control group, the behavioral experiments demonstrated that THz radiation significantly increased the distance travelled and speed of zebrafish larvae. In addition, the maximum acceleration and motion frequency were elevated in the 20 min radiation group. The neural Ca2+ imaging results indicated a substantial increase in zebrafish neuronal activity. qPCR experiments revealed a significant upregulation of dopamine-related genes, such as drd2b, drd4a, slc6a3 and th. (4) Conclusion: THz radiation (2.52 THz, 50 mW/cm2, 20 min) upregulated dopamine-related genes and significantly enhanced neuronal excitability, and the neurobiological effect of THz radiation can be visualized using neural Ca2+ imaging in vivo.
Advancing Fault Detection in HVAC Systems: Unifying Gramian Angular Field and 2D Deep Convolutional Neural Networks for Enhanced Performance
Authors: Tun Wunna , Wong Kwok-Wai (Johnny) , Ling Sai-Ho
Published: September 2023

Efficiency and comfort in buildings rely on on well-functioning HVAC systems. However, system faults can compromise performance. Modern data-driven fault detection methods, considering diverse techniques, encounter challenges in understanding intricate interactions and adapting to dynamic conditions present in HVAC systems during occupancy periods. Implementing fault detection during active operation, which aligns with real-world scenarios and captures dynamic interactions and environmental changes, is considered highly valuable. To address this, utilizing the dynamic simulation system HVAC SIMulation PLUS (HVACSIM+), an HVAC fault model was developed using 194 sensor signals from each HVAC component within a single-story, four-room building. The advanced HVAC fault detection framework, leveraging simulated HVAC operational scenarios with the Gramian angular field (GAF) and two-dimensional convolutional neural networks (GAF-2DCNNs), offers a robust and proactive solution. By utilizing the GAF capacity to convert time-series sensor data into informative 2D images, integrated with 2DCNN for automated feature extraction, hidden temporal relationships within 1D signals are captured. After training on nine significant HVAC faults and normal conditions during occupancy, the effectiveness of the proposed GAF-2DCNN is evaluated through comparisons with support vector machine (SVM), random forest (RF), and hybrid RF-SVM, one-dimensional convolutional neural networks (1D-CNNs). The results demonstrates an impressive overall accuracy of 97%, accompanied by precision, recall, and F1 scores that surpass 90% for individual HVAC faults. Through the introduction of the unified approach that integrates HVACSIM+ simulated data and GAF-2DCNN, a notable enhancement in robustness and reliability for handling substantial HVAC faults is achieved.
Quality Analysis and Correction of Sea Surface Temperature Data from China HY-1C Satellite in Southeast Asia Seas
Authors: Sun Weifu , Sangmanee Chalermrat , Jiang Yuanchi , Ma Yi , Li Jiang , Zhao Yujia
Published: September 2023

China’s marine satellite infrared radiometer SST remote sensing observations began relatively late. Thus, it is essential to evaluate and correct the SST observation data of the Ocean Color and Temperature Scanner (COCTS) onboard the China HY-1C satellite in the Southeast Asia seas. We conducted a quality assessment and correction work on the SST of the China COCTS/HY-1C in Southeast Asian seas based on multisource satellite SST data and temperature data measured by Argo buoys. The accuracy evaluation results of the COCTS SST indicated that the bias, Std, and RMSE of the daytime SST data for HY-1C were −0.73 °C, 1.38 °C, and 1.56 °C, respectively, while the bias, Std, and RMSE of the nighttime SST data were −0.95 °C, 1.57 °C, and 1.83 °C, respectively. The COCTS SST accuracy was significantly lower than that of other infrared radiometers. The effect of the COCTS SST zonal correction was most significant, with the Std and RMSE approaching 1 °C. After correction, the RMSE of the daytime SST and nighttime SST data decreased by 32.52% and 42.04%, respectively.
Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model
Authors: Zhang Chaozhu , Chu Hongxing , Ma Mingyuan
Published: September 2023

EEG decoding based on motor imagery is an important part of brain–computer interface technology and is an important indicator that determines the overall performance of the brain–computer interface. Due to the complexity of motor imagery EEG feature analysis, traditional classification models rely heavily on the signal preprocessing and feature design stages. End-to-end neural networks in deep learning have been applied to the classification task processing of motor imagery EEG and have shown good results. This study uses a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network to obtain spatial information and temporal correlation from EEG signals. The use of cross-layer connectivity reduces the network gradient dispersion problem and enhances the overall network model stability. The effectiveness of this network model is demonstrated on the BCI Competition IV dataset 2a by integrating CNN, BiLSTM and ResNet (called CLRNet in this study) to decode motor imagery EEG. The network model combining CNN and BiLSTM achieved 87.0% accuracy in classifying motor imagery patterns in four classes. The network stability is enhanced by adding ResNet for cross-layer connectivity, which further improved the accuracy by 2.0% to achieve 89.0% classification accuracy. The experimental results show that CLRNet has good performance in decoding the motor imagery EEG dataset. This study provides a better solution for motor imagery EEG decoding in brain–computer interface technology research.
A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model
Authors: Jiang Lingli , Sheng Heshan , Yang Tongguang , Tang Hujiao , Li Xuejun , Gao Lianbin
Published: September 2023

Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective.
Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
Authors: Sadad Tariq , Safran Mejdl , Khan Inayat , Alfarhood Sultan , Khan Razaullah , Ashraf Imran
Published: September 2023

Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and widespread availability for CVD management. In this research, we proposed an IoT-based monitoring and detection system for cardiac patients, employing a two-stage approach. In the initial stage, we used a routing protocol that combines routing by energy and link quality (REL) with dynamic source routing (DSR) to efficiently collect data on an IoT healthcare platform. The second stage involves the classification of ECG images using hybrid-based deep features. Our classification system utilizes the “ECG Images dataset of Cardiac Patients”, comprising 12-lead ECG images with four distinct categories: abnormal heartbeat, myocardial infarction (MI), previous history of MI, and normal ECG. For feature extraction, we employed a lightweight CNN, which automatically extracts relevant ECG features. These features were further optimized through an attention module, which is the method’s main focus. The model achieved a remarkable accuracy of 98.39%. Our findings suggest that this system can effectively aid in the identification of cardiac disorders. The proposed approach combines IoT, deep learning, and efficient routing protocols, showcasing its potential for improving CVD diagnosis and management.
Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking
Authors: Shams Ramiza , Abdrabou Atef , Al Bataineh Mohammad , Noordin Kamarul Ariffin
Published: September 2023

Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes.
Monitoring Sensors for Urban Air Quality: The Case of the Municipality of Lisbon
Authors: Sarroeira Rodrigo , Henriques João , Sousa Ana M. , Ferreira da Silva Catarina , Nunes Nuno , Moro Sérgio , Botelho Maria do Carmo
Published: September 2023

Air pollution is a global issue that impacts environmental inequalities, and air quality sensors can have a decisive role in city policymaking for future cities. Science and society are already aware that during the most challenging times of COVID-19, the levels of air pollution in cities decreased, especially during lockdowns, when road traffic was reduced. Several pollution parameters can be used to analyse cities’ environmental challenges, and it is more pressing than ever to have city climate decisions supported by sensor data. We have applied a data science approach to understand the evolution of the levels of carbon monoxide, nitrogen dioxide, particulate matter 2.5, and particulate matter 10 between August 2021 and July 2022. The analysis of the air quality levels, captured for the first time via 80 monitoring stations distributed throughout the municipality of Lisbon, has allowed us to realize that nitrogen dioxide and particulate matter 10 exceed the levels that are recommended by the World Health Organization, thereby increasing the health risk for those who live and work in Lisbon. Supported by these findings, we propose a central role for air quality sensors for policymaking in future cities, taking as a case study the municipality of Lisbon, Portugal, which is among the European cities that recently proposed be climate-neutral and smart city by 2030.
Preventing Forklift Front-End Failures: Predicting the Weight Centers of Heavy Objects, Remaining Useful Life Prediction under Abnormal Conditions, and Failure Diagnosis Based on Alarm Rules
Authors: Lee Jeong-Geun , Kim Yun-Sang , Lee Jang Hyun
Published: September 2023

This paper addresses the critical challenge of preventing front-end failures in forklifts by addressing the center of gravity, accurate prediction of the remaining useful life (RUL), and efficient fault diagnosis through alarm rules. The study’s significance lies in offering a comprehensive approach to enhancing forklift operational reliability. To achieve this goal, acceleration signals from the forklift’s front-end were collected and processed. Time-domain statistical features were extracted from one-second windows, subsequently refined through an exponentially weighted moving average to mitigate noise. Data augmentation techniques, including AWGN and LSTM autoencoders, were employed. Based on the augmented data, random forest and lightGBM models were used to develop classification models for the weight centers of heavy objects carried by a forklift. Additionally, contextual diagnosis was performed by applying exponentially weighted moving averages to the classification probabilities of the machine learning models. The results indicated that the random forest achieved an accuracy of 0.9563, while lightGBM achieved an accuracy of 0.9566. The acceleration data were collected through experiments to predict forklift failure and RUL, particularly due to repeated forklift use when the centers of heavy objects carried by the forklift were skewed to the right. Time-domain statistical features of the acceleration signals were extracted and used as variables by applying a 20 s window. Subsequently, logistic regression and random forest models were employed to classify the failure stages of the forklifts. The F1 scores (macro) obtained were 0.9790 and 0.9220 for logistic regression and random forest, respectively. Moreover, random forest probabilities for each stage were combined and averaged to generate a degradation curve and determine the failure threshold. The coefficient of the exponential function was calculated using the least squares method on the degradation curve, and an RUL prediction model was developed to predict the failure point. Furthermore, the SHAP algorithm was utilized to identify significant features for classifying the stages. Fault diagnosis using alarm rules was conducted by establishing a threshold derived from the significant features within the normal stage.
Emerging Technologies for 6G Communication Networks: Machine Learning Approaches
Authors: Puspitasari Annisa Anggun , An To Truong , Alsharif Mohammed H. , Lee Byung Moo
Published: September 2023

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.
Shaping of HKUST-1 via Extrusion for the Separation of CO2/CH4 in Biogas
Authors: Rozaini Muhamad Tahriri , Grekov Denys I. , Bustam Mohamad Azmi , Pré Pascaline
Published: September 2023

HKUST-1 is a metal-organic framework (MOF) that is widely studied as an adsorbent for CO2 capture because of its high adsorption capacity and good CO2/CH4 selectivity. However, the numerous synthesis routes for HKUST-1 often result in the obtention of MOF in powder form, which limits its application in industry. Here, we report the shaping of HKUST-1 powder via the extrusion method with the usage of bio-sourced polylactic acid (PLA) as a binder. The characterization of the composite was determined by XRD, FTIR, TGA and SEM analyses. The specific surface area was determined from the N2 adsorption isotherm, whereas the gas adsorption capacities were investigated via measurements of CO2 and CH4 isotherms of up to 10 bar at ambient temperature. The material characterization reveals that the composite preserves HKUST-1’s crystalline structure, morphology and textural properties. Furthermore, CO2 and CH4 adsorption isotherms show that there is no degradation of gravimetric gas adsorption capacity after shaping and the composite yields a similar isosteric adsorption heat as pristine HKUST-1 powder. However, some trade-offs could be observed, as the composite exhibits a lower bulk density than pristine HKUST-1 powder and PLA has no impact on pristine HKUST-1’s moisture stability. Overall, this study demonstrates the possibility of shaping commercial HKUST-1 powder, using PLA as a binder, into a larger solid-state-form adsorbent that is suitable for the separation of CO2 from CH4 with a well-preserved pristine MOF gas-adsorption performance.
Application of Coffee Husk Ash as Partial Replacement of Fine Aggregate in Concrete
Authors: Bhandary Radhika P. , Rao Asha U. , Shetty Prathibha P. , Blesson S. , Thomas Blessen Skariah
Published: September 2023

The task of turning agricultural waste into practical construction and building materials has been placed before civil engineers. Coffee husk is produced in vast amounts due to the global commerce of coffee beans, which are incinerated into ash when used as fuel, producing coffee husk ash (CHA). Even though many researchers have worked on the utilization of CHA in concrete, they have been used as partial cement replacement but not as a replacement of aggregates. The experimental study of the performance of concrete on fine aggregate replaced partially with CHA is represented in this paper. The fine aggregate is replaced by 0%, 2%, 4%, 6%, and 8% by weight of CHA. The performance of the partially replaced fine aggregate with CHA is reviewed by considering the compressive strength and workability of fresh concrete and the splitting tensile strength, flexural strength, durability under acid and alkaline media, thermal conductivity, and rapid chloride permeability test of hardened concrete. The results indicate that the partial replacement of fine aggregate with 4% of CHA (CHA04) in concrete provides a positive impact to all the selected performance parameters. The compressive strength, flexural strength, and splitting tensile of the CHA04 mix were 43.4 MPa, 3.7 MPa, and 2.44 MPa, respectively, which were 28.4%, 19.35%, and 1.66%, respectively, greater than normal concrete mix (CHA00). Even the study of acid and alkaline attack on the CHA04 mix showed lesser strength reduction as compared to other mixes. The RCPT showed less chloride permeability, and the thermal conductivity is higher for CHA04, indicating lesser voids compared to other mixes. With the help of this investigation, it can be said that fine aggregate replacement with 4% CHA has the best strength and durability properties compared to regular concrete.
The Role in the Human Diet of Bioaccumulation of Selenium, Copper, Zinc, Manganese and Iron in Edible Mushrooms in Various Habitat Conditions of NW Poland—A Case Study
Authors: Sotek Zofia , Stasińska Małgorzata , Malinowski Ryszard , Pilarczyk Bogumiła , Pilarczyk Renata , Bąkowska Małgorzata , Malinowska Katarzyna , Radke Patrycja , Kubus Marcin , Malinowska Alicja , Bukowska Aleksandra
Published: September 2023

The aim of the study was to determine the contents of microelements in Boletus edulis, Imleria badia and Leccinum scabrum, taking into account the soil conditions in selected forest areas of Northwest Poland and the bioaccumulation capacity of these fungi and their role in the human diet. Se, Cu, Zn, Mn and Fe contents were determined in the soil (organic and mineral layers) and mushrooms. The study showed that the soils on which fruiting bodies grew did not differ significantly in the contents of these trace elements. The concentrations of microelements in mushrooms in NW Poland were mostly at the lower range of the contents reported for these species in other regions of Poland and Europe. The uptake of microelements by the studied mushrooms was influenced by soil reaction, organic matter content, and bioavailable and total forms of the elements. B. edulis contained significantly more Se than other mushroom species and, together with I. badia, was much more abundant in Cu and Zn than L. scabrum. Fruiting bodies bioaccumulated Se (most strongly by B. edulis; BCF = 120.6), Cu and Zn. The contents of microelements in the tested mushrooms may be supplementary elements in the human diet.
Urban Spatial Strategies of the Gulf Cooperation Council: A Comparative Analysis and Lessons Learned
Authors: Arif Mohammad , Aldosary Adel S.
Published: September 2023

Gulf Cooperation Council (GCC) members have experienced tremendous transformation in their smart cities in recent years. Every GCC nation has its own urban planning authority to manage urban growth and development since its independence from British rule. These planning agencies create strategies, plans, rules, and oversee the building process. The novelty of this research lies in its comprehensive analysis, cross-border comparisons, and the generation of insights that contribute to a deeper understanding of urban planning dynamics and strategies within the Gulf Cooperation Council. The objective of this article is to examine the national spatial strategies (NSS) and vision plans of GCC countries. This study discusses the existing methods, plans, and efforts to accomplish this goal while emphasizing prospective opportunities, problems, and difficulties. We used the SWOT method to evaluate the national vision plans of GCC countries. The analysis identifies successful outcomes that the GCC countries have already achieved in terms of their national spatial strategies. The weakness in the current strategies is oil dependency, which could be strengthened. Potential opportunities in the tourism sector need to be taken full advantage of, and potential threats, like regional tension, need to be managed to prevent the failure of the development of the existing urban system. The primary suggestion entails executing programs outlined by the respective line ministries and ensuring efficient management of urban spatial expansion by municipal authorities. However, the findings could serve as lessons for those at the helm of affairs in GCC countries to collaborate and achieve a comprehensive national strategic plan.
Cities in Transition and Urban Innovation Ecosystems: Place and Innovation Dynamics in the Case of Boston and Cambridge (USA)
Authors: Bevilacqua Carmelina , Pizzimenti Pasquale , Ou Yapeng
Published: September 2023

Urban transition is gaining relevance in the academic and policy debate for rethinking urban development strategies toward resilience and sustainability. The transformative power of innovation and knowledge is called upon to speed up the process. In this paper, we address the issue of urban transition by exploring how the urban innovation ecosystem is a crucial factor in operationalizing city transition strategies. For this scope, we propose a methodological approach to identify the city-level innovation ecosystem by connecting innovation dynamics with urban transformation. The objective is to highlight how transition dynamics are spurred by the urban innovation ecosystem in its maturity stage. Therefore, the paper proposes a case study of the Boston area (the cities of Boston and Cambridge, USA) where it is possible to detect a mature urban innovation ecosystem. The case analysis unveiled the urban characterizing factors of the innovation ecosystem. Here, the concentration of innovation activities stimulates the demand for urban transformations, which are managed through urban planning and zoning and specific supportive policy-planning initiatives.
Influence of Blast Furnace Slag on Pore Structure and Transport Characteristics in Low-Calcium Fly-Ash-Based Geopolymer Concrete
Authors: Azimi Zahir , Toufigh Vahab
Published: September 2023

Alkali-activated fly ash slag (AAFS) has emerged as a novel and environmentally sustainable construction material, garnering substantial attention due to its commendable mechanical attributes and minimal ecological footprint. This investigation delves into the influence of slag incorporation on the strength, pore structure, and transport characteristics of AAFS, encompassing various levels of fly ash replacement with slag. To assess the mechanical properties of AAFS concrete, unconfined compression and ultrasonic pulse velocity tests were conducted. Meanwhile, microstructural and mineralogical alterations were scrutinized through porosity, N2-adsorption/desorption, and SEM/EDX assessments. In addition, transport properties were gauged using electrical surface resistivity, water permeability, and water vapor permeability tests. According to the results, a remarkable refinement in the pore volume was found by increasing the slag content. The volume of the gel pores and surface area increased significantly associated with the increase in tortuosity. Accordingly, Ca inclusion in the cross-linked sodium aluminosilicate hydrate gel remarkably reduced the transport properties.
A Comprehensive Methodological Approach for the Assessment of Urban Identity
Authors: Mansour Hasan Mahmoud , Alves Fernando Brandão , da Costa António Ricardo
Published: September 2023

Urban identity, defined as the dynamic interplay between individuals and their environment, reciprocally shapes each other within the context of unique and defining characteristics of an urban locale. This concept has served as rich fodder for intellectual discourse, yet practical, applied studies have been restricted to local-scale “place identity” or linked to urban preservation and heritage concepts. Such constraints confine the understanding of urban identity to a retrospective view, leaving its contemporaneous and prospective dimensions underexplored. Furthermore, the multi-dimensional nature of urban identity, encompassing social, cultural, historical, and spatial elements, has rendered it a complex phenomenon to define and measure, leading to its neglect in applied studies. In an era when cities face multifaceted challenges ranging from climate change to socioeconomic disparities, understanding and leveraging urban identity becomes more than a matter of civic pride; it is a strategic necessity. Addressing this, our central research question asks: “Can we develop an integrated and dynamic methodology for assessing urban identity that is applicable to various case studies, accounting for its multifaceted nature and constant evolution, and can this methodology steer urban planning and policy decisions?” To answer these questions in detail, this study was divided into two phases. The first phase identifies and examines descriptive and analytical approaches to identity and urban identity, evaluating processes and highlighting key debates, issues, and gaps. The second phase constructs a methodology for assessing urban identity based on insights from the theoretical analysis. Through this research, three main dimensions in urban identity are unveiled: spatial scale, observer, and the dynamic state over time which framed our proposed methodology. By considering these dimensions and categorising components into material and immaterial dimensions, an enriched view of urban identity, capturing its dynamic and evolving nature is reached. Incorporating seven data collection methods, it classifies urban identity into three distinct states: “stable, fragmented, and lost”, enabling strategic interventions to foster favourable transformations. This methodology serves as a tool to address contemporary challenges such as climate change, socioeconomic disparities, and public safety, thereby transforming the traditional understanding of urban identity.
Improved Multi-Person 2D Human Pose Estimation Using Attention Mechanisms and Hard Example Mining
Authors: Zhang Lixin , Huang Wenteng , Wang Chenliang , Zeng Hui
Published: September 2023

In recent years, human pose estimation, as a subfield of computer vision and artificial intelligence, has achieved significant performance improvements due to its wide applications in human-computer interaction, virtual reality, and smart security. However, most existing methods are designed for single-person scenes and suffer from low accuracy and long inference time in multi-person scenes. To address this issue, increasing attention has been paid to developing methods for multi-person pose estimation, such as utilizing Partial Affinity Field (PAF)-based bottom-up methods to estimate 2D poses of multiple people. In this study, we propose a method that addresses the problems of low network accuracy and poor estimation of flexible joints. This method introduces the attention mechanism into the network and utilizes the joint point extraction method based on hard example mining. Integrating the attention mechanism into the network improves its overall performance. In contrast, the joint point extraction method improves the localization accuracy of the flexible joints of the network without increasing the complexity. Experimental results demonstrate that our proposed method significantly improves the accuracy of 2D human pose estimation. Our network achieved a notably elevated Average Precision (AP) score of 60.0 and outperformed competing methods on the standard benchmark COCO test dataset, signifying its exceptional performance.
Study on Overburden Fracture and Structural Distribution Evolution Characteristics of Coal Seam Mining in Deep Large Mining Height Working Face
Authors: Zhang Jianguo , Qin Xiaofeng , Liu Shuaitao , Su Haijian , Yang Zhanbiao , Zhang Guochuan
Published: September 2023

Coal mining has gradually entered the deep mining era, and large-height mining is an important way to mine thick coal seams in the deep. The high coal wall will inevitably make the distribution of the overburden structure in the coal mining face more complicated, and the large buried depth will also cause more intense mine pressure. The study of the distribution and evolution of the overburden structure and stress in the mining site can provide theoretical guidance for safe mining. In this work, a physical similarity modeling test was carried out based on the physical–mechanical parameters of overburden rock and similarity theory, taking the mining of a deep, large-height working face in Pingdingshan Coal Mine as an example. The results show that the deformation and breakage of overburden rock in deep, large-height workings occurring during mining is persistent and not only in a short period of time. The breakage form of overburden can be categorized into two types based on the deformation characteristics: (I) non-separation-induced type, and (II) separation-induced type. Among these, the breakage induced by separation can be divided into two categories: (i) dominated by self-weight stress, and (ii) affected by shear cracks. It also summarizes the form of the overburden structure and the structural morphology of the stope. The overburden structure shows a “combined cantilever beam structure-articulated rock-slab structure-non-articulated rock-slab structure”. Among these, the periodic breakage of the upper cantilever beam evolved articulated and non-articulated rock-slab structure in the lower part, which weakened the supporting effect of the lower gangue and further aggravated the breakage of the upper overburden rock. The shape of the main structure of the stope mainly depends on the fracture line from the advancing coal wall to the upper overburden: from a rectangular shape without collapse to a trapezoidal shape at the initial stage of collapse, to a trapezoidal shape with multiple steps after the main roof collapse.
WNMS: A New Basaltic Simulant of Mars Regolith
Authors: Rahim Abdur , Majeed Umair , Zubair Muhammad Irfan , Shahzad Muhammad
Published: September 2023

The use of planetary regolith can be explored via the utilization of simulants. The existing Martian simulants have differences due to varying source materials and design parameters. Additional simulants are needed because the few available simulants do not replicate the compositional diversity of Martian regolith. This study discusses the development of a low-cost construction simulant of Mars. The area of Winder Nai in Pakistan was selected for field sampling of basalt because of local availability and easy access. The dust was produced from rock samples through mechanical crushing and grinding. The physical properties, composition, mineralogy, and surface morphology were evaluated via geotechnical tests, Energy Dispersive X-ray (EDX) spectroscopy, X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM), respectively. The designed simulant has a well-graded particle size distribution with a particle density and bulk density of 2.58 g/cm3 and 1.16 g/cm3, respectively. The elemental composition of Winder Nai Mars Simulant (WNMS) is within ±5 wt% of the Rocknest and the average Martian regolith composition except for SO3. For SiO2, Al2O3, and Fe2O3, WNMS has a good match with the Martian regolith. The content of CaO and TiO2 in WNMS is higher than, and content of MgO is lower than, the average Martian values. The rock can be classified as basalt based on the Total Alkali Silica (TAS) diagram. XRD spectrum indicates the occurrence of plagioclase and pyroxene as the main signature minerals of basalt. The particle morphology of WNMS is angular to subangular, and the simulant indicates the presence of 3.8 wt% highly paramagnetic particles. The volatile loss is 0.25 wt% at 100 °C, 1.73 wt% at 500 °C, and 3.05 wt% at 950 °C. The composition of WNMS, basaltic mineralogy, morphology, magnetic properties, and volatile content are comparable with MMS-2 and a few other simulants.
Analysis of the Spatial and Temporal Evolution of Energy-Related CO2 Emissions in China’s Coastal Areas and the Drivers of Industrial Enterprises above Designated Size—The Case of 82 Cities
Authors: Duan Ye , Zhong Juanjuan , Wang Hongye , Sun Caizhi
Published: September 2023

The energy consumption by industrial enterprises above designated size in China’s coastal region is the main source of CO2 emissions. This study analyzes the spatial and temporal evolution patterns and driving factors of CO2 emissions due to the energy consumption by industrial enterprises above designated size. Enterprises in 82 cities in China’s coastal regions were studied from 2005 to 2020 based on their CO2 emissions and socio-economic data. The Exploring Spatial Data Analysis (ESDA) methodology and Logarithmic mean Divisia Index decomposition (LMDI model) were used. The results show that, during the study period, energy-related CO2 emissions from industrial enterprises above designated size in China’s coastal areas generally show a fluctuating upward trend. However, a few cities showed a trend from steady growth to a peak and then a slow decline, which may realize the “double carbon” target in advance. The spatial correlation of CO2 emission intensity showed a decreasing and then increasing trend, and there were spatial aggregation characteristics in some cities. Among the driving factors, the pull effect is higher than the inhibition effect; the output scale contributes the most to the pull effect, and labor productivity contributes the most to the inhibition effect. The results of this study have a certain reference value for the realization of the “double carbon” target in China’s coastal regions.
Indwelling Vascular Access Ports: Application, Advantages, and Management in Nonhuman Primates
Authors: Oppler Scott H. , Leishman David J. , Graham Melanie L.
Published: September 2023

Animals in the veterinary and experimental settings, including nonhuman primates (NHPs), often require repeated and prolonged vascular access for indications including blood sampling or administration of fluids, blood products, medication, or other therapies. A vascular access approach should be tailored to experimental or clinical use meeting the needs of the individual animal such that benefits outweigh risks. The optimal device and placement technique is based on the inherent advantages and disadvantages of specific anatomic sites and planned use. Totally implanted vascular access ports (VAPs) enable reliable central venous access for frequent sample collection and/or intravenous therapies. VAPs minimize discomfort with IV access to facilitate cooperation with handling and minimize stress-induced physiologic changes which can confound biologic data and drug responses. VAPs do not limit species-typical behavior and social group activities and are compatible with animal enrichment programs that include play and swim because there are no externalized components. VAPs are typically used long-term and demonstrate excellent durability with high patency and low complication rates over time, presenting a safe and dependable vascular access approach.
The Presence of Pseudo-nitzschia australis in North Atlantic Aquaculture Sites, Implications for Monitoring Amnesic Shellfish Toxins
Authors: Whyte Callum , Swan Sarah C. , Turner Andrew D. , Hatfield Robert G. , Mitchell Elaine , Lafferty Shannon , Morrell Nadine , Rowland-Pilgrim Stepahanie , Davidson Keith
Published: September 2023

The farming of shellfish plays an important role in providing sustainable economic growth in coastal, rural communities in Scotland and acts as an anchor industry, supporting a range of ancillary jobs in the processing, distribution and exporting industries. The Scottish Government is encouraging shellfish farmers to double their economic contribution by 2030. These farmers face numerous challenges to reach this goal, among which is the problem caused by toxin-producing microplankton that can contaminate their shellfish, leading to harvesting site closure and the recall of product. Food Standards Scotland, a non-ministerial department of the Scottish Government, carries out a monitoring programme for both the toxin-producing microplankton and the toxins in shellfish flesh, with farms being closed when official thresholds for any toxin are breached. The farm remains closed until testing for the problematic toxin alone, often diarrhetic shellfish toxin (DST), shows the site to have dropped below the regulatory threshold. While this programme has proved to be robust, questions remain regarding the other toxins that may be present at a closed site. In this study, we tested archival material collected during site closures but only tested for DSTs as part of the official control monitoring. We found the presence of amnesic shellfish toxin (AST) in low concentrations in the majority of sites tested. In one case, the level of AST breached the official threshold. This finding has implications for AST monitoring programmes around Europe.
Recurrent Immunoglobulin A Nephropathy after Kidney Transplant—An Updated Review
Authors: Han Hwarang S. , Lubetzky Michelle L. , Anandasivam Nidharshan S. , Cox Rebecca A. , Lee Brian K.
Published: September 2023

Immunoglobulin A nephropathy (IgAN) is the commonest glomerulonephritis worldwide, a category that represents the third most frequent cause of end-stage kidney disease (ESKD) in the United States. Kidney transplantation remains the optimal treatment of ESKD, and yet the prospects of IgAN recurrence post-transplant dampens the enthusiasm for living kidney donation in some instances, in addition to limiting the longevity of the kidney allograft. Moreover, the lack of a standardized method for detecting IgAN recurrence, since not all centers perform protocol allograft biopsies, has led to an underestimation of the extent of the issue. The pathogenesis of de novo IgAN remains conjectural, let alone the pathways for recurrent disease, but is increasingly recognized as a multi-hit injury mechanism. Identification of recurrent disease rests mainly on clinical symptoms and signs (e.g., hematuria, proteinuria) and could only be definitively proven with histologic evidence which is invasive and prone to sampling error. Treatment had relied mainly on nonspecific goals of proteinuria reduction, and in some cases, immunosuppression for active, crescentic disease. More recently, newer targets have the potential to widen the armamentarium for directed therapies, with more studies on the horizon. This review article provides an update on recurrent IgAN post-transplant.
One Health Approach to Leptospirosis: Dogs as Environmental Sentinels for Identification and Monitoring of Human Risk Areas in Southern Brazil
Authors: Sohn-Hausner Natacha , Kmetiuk Louise Bach , da Silva Evelyn Cristine , Langoni Helio , Biondo Alexander Welker
Published: September 2023

Leptospirosis has been a neglected, widespread and reemerging zoonotic disease of global importance. The One Health holistic approach combining human, animal and environmental health has been important for control and prevention of zoonotic disease. An urban municipality in Southern Brazil with a high prevalence of leptospirosis was selected by convenience, with asymptomatic human and canine individuals serologically tested using MAT against 30 Leptospira spp. serovars. Epidemiological questionnaires were assessed along with the historical national database, with associated risk factors to dog and human seropositivity analyzed using multiple logistic regression. The spatial distribution of retrospective human leptospirosis cases was analyzed using a Kernel map and overlapped dog seropositivity and historical flooding areas, demonstrating that domestic and asymptomatic dogs may be important environmental sentinels for leptospirosis in such urban areas, even in the absence of human seropositivity and low canine seropositivity. Associated risk factors for leptospirosis in dogs and humans according to multiple regression included healthy dogs (p = 0.02, odds ratio—OR = 0.17, confidence interval of 95%CI 0.03–0.71) with indoor access (p = 0.059, OR = 5.12, 95%CI 1.10–37.7) and human males (p = 0.042, OR = 2.44, 95%CI 1.08–6.11) with presenting calf pain (p = 0.005, OR = 3.14, 95%CI 1.46–7. 14), jaundice (p < 0.001, OR = 4.61, 95%CI 2.11–10.10) and renal failure (p = 0.008, OR = 4.49, 95%CI 1.49–13.76). The greater the rain precipitation (above 3 mm of average annual precipitation), the higher the number of leptospirosis cases in humans. In conclusion, dog active serosurvey and rain precipitation should be systematically reported and plotted altogether as a basis for the early detection and monitoring of human risk areas. Such findings may serve as a basis for public health policies in Brazil and other endemic countries worldwide.
Mannose-Binding Lectins as Potent Antivirals against SARS-CoV-2
Authors: Grosche Victória Riquena , Souza Leandro Peixoto Ferreira , Ferreira Giulia Magalhães , Guevara-Vega Marco , Carvalho Tamara , Silva Romério Rodrigues dos Santos , Batista Karla Lilian Rodrigues , Abuna Rodrigo Paolo Flores , Silva João Santana , Calmon Marília de Freitas , Rahal Paula , da Silva Luis Cláudio Nascimento , Andrade Bruno Silva , Teixeira Claudener Souza , Sabino-Silva Robinson , Jardim Ana Carolina Gomes
Published: September 2023

The SARS-CoV-2 entry into host cells is mainly mediated by the interactions between the viral spike protein (S) and the ACE-2 cell receptor, which are highly glycosylated. Therefore, carbohydrate binding agents may represent potential candidates to abrogate virus infection. Here, we evaluated the in vitro anti-SARS-CoV-2 activity of two mannose-binding lectins isolated from the Brazilian plants Canavalia brasiliensis and Dioclea violacea (ConBR and DVL). These lectins inhibited SARS-CoV-2 Wuhan-Hu-1 strain and variants Gamma and Omicron infections, with selectivity indexes (SI) of 7, 1.7, and 6.5, respectively for ConBR; and 25, 16.8, and 22.3, for DVL. ConBR and DVL inhibited over 95% of the early stages of the viral infection, with strong virucidal effect, and also protected cells from infection and presented post-entry inhibition. The presence of mannose resulted in the complete lack of anti-SARS-CoV-2 activity by ConBR and DVL, recovering virus titers. ATR-FTIR, molecular docking, and dynamic simulation between SARS-CoV-2 S and either lectins indicated molecular interactions with predicted binding energies of −85.4 and −72.0 Kcal/Mol, respectively. Our findings show that ConBR and DVL lectins possess strong activities against SARS-CoV-2, potentially by interacting with glycans and blocking virus entry into cells, representing potential candidates for the development of novel antiviral drugs.