bluuebunny/crossref_metadata_2025 huggingface dataset:
Methylphenidate Multiphasic Release Tablet: Bioequivalence Assessment between Two Formulations Administered under Fasting and Fed Conditions
Authors: Davanço Marcelo Gomes , Meulman Jessica , da Silva Thalita Martins , Costa Fernando , Bellorio Karini Bruno , Mundim Iram Moreira , Sampaio Ana Carolina Costa , Teixeira Leonardo de Souza , Vespasiano Celso Francisco Pimentel
Published: June 2023

Methylphenidate hydrochloride is used to treat children, adolescents, and adults with attention deficit/hyperactivity disorder (ADHD). Multiphasic release formulation has been used to control drug levels, mainly during children’s school period. This study aimed to evaluate the bioequivalence between two methylphenidate hydrochloride extended-release tablets to meet regulatory requirements for registration in Brazil. Two independent studies (under fasting and fed conditions) designed as open-label, randomized, single-dose, two-period, two-way crossover trials were conducted in healthy subjects of both genders. Subjects were enrolled and randomly received a single dose of the test formulation methylphenidate hydrochloride 54 mg extended-release tablet (Consiv®, Adium S.A., São Paulo, Brazil) or the reference formulation (Concerta®, Janssen-Cilag Farmacêutica Ltd., São Paulo, Brazil), in each period, with a 7-day washout interval. Serial blood samples were collected up to 24 h post dose and methylphenidate plasma concentrations were obtained using a validated LC-MS/MS method. A total of 96 healthy subjects were enrolled in the fasting study, of which 80 completed the study. For the fed study, 52 healthy subjects were enrolled, and 46 subjects completed it. In both studies, 90% confidence intervals for Cmax, AUC0–t, AUC0–inf, and partial AUCs were within the acceptable limits of 80.00 to 125.00%. Thus, according to regulatory requirements, the test formulation (Consiv®) was considered to be bioequivalent to the reference formulation (Concerta®) in both conditions (fasting and fed) and, therefore, it can be considered interchangeable in clinical practice. Both formulations were safe and well tolerated in single-dose administration.
Smart Nanocarriers for the Targeted Delivery of Therapeutic Nucleic Acid for Cancer Immunotherapy
Authors: Baker Abu , Lorch Jochen , VanderWeele David , Zhang Bin
Published: June 2023

A wide variety of therapeutic approaches and technologies for delivering therapeutic agents have been investigated for treating cancer. Recently, immunotherapy has achieved success in cancer treatment. Successful clinical results of immunotherapeutic approaches for cancer treatment were led by antibodies targeting immune checkpoints, and many have advanced through clinical trials and obtained FDA approval. A major opportunity remains for the development of nucleic acid technology for cancer immunotherapy in the form of cancer vaccines, adoptive T-cell therapies, and gene regulation. However, these therapeutic approaches face many challenges related to their delivery to target cells, including their in vivo decay, the limited uptake by target cells, the requirements for nuclear penetration (in some cases), and the damage caused to healthy cells. These barriers can be avoided and resolved by utilizing advanced smart nanocarriers (e.g., lipids, polymers, spherical nucleic acids, metallic nanoparticles) that enable the efficient and selective delivery of nucleic acids to the target cells and/or tissues. Here, we review studies that have developed nanoparticle-mediated cancer immunotherapy as a technology for cancer patients. Moreover, we also investigate the crosstalk between the function of nucleic acid therapeutics in cancer immunotherapy, and we discuss how nanoparticles can be functionalized and designed to target the delivery and thus improve the efficacy, toxicity, and stability of these therapeutics.
Designing Electrical Stimulation Platforms for Neural Cell Cultivation Using Poly(aniline): Camphorsulfonic Acid
Authors: Garrudo Fábio F. F. , Linhardt Robert J. , Ferreira Frederico Castelo , Morgado Jorge
Published: June 2023

Electrical stimulation is a powerful strategy to improve the differentiation of neural stem cells into neurons. Such an approach can be implemented, in association with biomaterials and nanotechnology, for the development of new therapies for neurological diseases, including direct cell transplantation and the development of platforms for drug screening and disease progression evaluation. Poly(aniline):camphorsulfonic acid (PANI:CSA) is one of the most well-studied electroconductive polymers, capable of directing an externally applied electrical field to neural cells in culture. There are several examples in the literature on the development of PANI:CSA-based scaffolds and platforms for electrical stimulation, but no review has examined the fundamentals and physico-chemical determinants of PANI:CSA for the design of platforms for electrical stimulation. This review evaluates the current literature regarding the application of electrical stimulation to neural cells, specifically reviewing: (1) the fundamentals of bioelectricity and electrical stimulation; (2) the use of PANI:CSA-based systems for electrical stimulation of cell cultures; and (3) the development of scaffolds and setups to support the electrical stimulation of cells. Throughout this work, we critically evaluate the revised literature and provide a steppingstone for the clinical application of the electrical stimulation of cells using electroconductive PANI:CSA platforms/scaffolds.
Developing a Hybrid Algorithm Based on an Equilibrium Optimizer and an Improved Backpropagation Neural Network for Fault Warning
Authors: Liu Jiang , Zhan Changshu , Wang Haiyang , Zhang Xingqin , Liang Xichao , Zheng Shuangqing , Meng Zhou , Zhou Guishan
Published: June 2023

In today’s rapidly evolving manufacturing landscape with the advent of intelligent technologies, ensuring smooth equipment operation and fostering stable business growth rely heavily on accurate early fault detection and timely maintenance. Machine learning techniques have proven to be effective in detecting faults in modern production processes. Among various machine learning algorithms, the Backpropagation (BP) neural network is a commonly used model for fault detection. However, due to the intricacies of the BP neural network training process and the challenges posed by local minima, it has certain limitations in practical applications, which hinder its ability to meet efficiency and accuracy requirements in real-world scenarios. This paper aims to optimize BP networks and develop more effective fault warning methods. The primary contribution of this research is the proposal of a novel hybrid algorithm that combines a random wandering strategy within the main loop of an equilibrium optimizer (EO), a local search operator inspired by simulated annealing, and an adaptive learning strategy within the BP neural network. Through analysis and comparison of multiple sets of experimental data, the algorithm demonstrates exceptional accuracy and stability in fault warning tasks, effectively predicting the future operation of equipment and systems. This innovative approach not only overcomes the limitations of traditional BP neural networks, but also provides an efficient and reliable solution for fault detection and early warning in practical applications.
Gender, Education and Citizenship as Ideological Weapons of an ‘Army of Holy Women’ in Bengal: The Matua Matri Sena
Authors: Sarbadhikary Sukanya , Roy Dishani
Published: June 2023

This paper seeks to analyze the recent phenomenon of the development of a Matri Sena (literally, an ‘Army of Holy Women’) among the Matua sect of West Bengal, India. Historically known to have suffered caste-based untouchability and forced migration due to communal conflict, the Matua community’s current political motivations surround the issue of ‘refugeehood’ and Indian citizenship. Given this background, the emergence of the Matri Sena as a complex identity among a religion–caste–gender–nation nexus is oriented towards bipartite objectives: one, to actualize the gender-egalitarian ethos that informs Matua religious foundations, and two, to claim legal citizenship status for its community members precisely through a new gendered ideology. We argue that the women gurus of the Matri Sena are able to realize their religious/political aims by fashioning themselves as mothers of an ideal family, community, and by extension, the nation. In deploying their specific gendered ideological constructions, they enact their new roles as influencers in both private and public Matua lives. In such renderings, the woman guru’s mother-figure emerges as a political subject through crucial engagements with Matua religiosity on one hand, and dominant Hindu nationalist discourses on the other. In this article, we critically analyze ways in which the Matri Sena constructs a new maternal notion of religio-political power, and how such power furthers both collective Matua aspirations and contemporary national imaginations.
Shakti in Village India: Priestesses, Sadhikas, Bhar Ladies, Ayes, Bhaktas, Witches, and Bonga Girls
Authors: McDaniel June
Published: June 2023

In this paper, we shall examine some major religious roles for women in West Bengal, India, and the challenges they must face. Among the Santals, an Adivasi group, religious women must avoid being called witches, for women’s power is seen as dangerous and religious social roles are traditionally forbidden to them. Some women have been called by deities to become trance mediums, colloquially known as ‘bhar ladies’, and this role is generally not accepted by family members. Girls have had to undergo exorcisms by male healers to get them to renounce the gods that have called them to this role, while married women must deal with husbands who do not want their wives going into public trances. Many such women have learned tantric practices to control the trance possession. In rural areas, the combination of ascetic practices and stories known as bratas (vratas) are taught to young girls by female leaders called ayes. However, in more urban areas, this role has been taken over by male brahmin priests. We also see women in the bhakti tradition, who run ashrams and lead worship and who must deal with male devotees who question a woman’s leadership abilities. All of these involve challenges, and many of these women have developed strategies to deal with the difficulties of being a religious influencer in their societies.
A Cognitive Electronic Jamming Decision-Making Method Based on Q-Learning and Ant Colony Fusion Algorithm
Authors: Zhang Chudi , Song Yunqi , Jiang Rundong , Hu Jun , Xu Shiyou
Published: June 2023

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε-greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence.
Remote Sensing Analysis of the Surface Urban Heat Island Effect in Vitoria-Gasteiz, 1985 to 2021
Authors: Errea Cristina Laurenti , Almeida Cátia Rodrigues de , Gonçalves Artur , Teodoro Ana Cláudia
Published: June 2023

Vitoria-Gasteiz has taken several urban greening actions such as the introduction of a ring of parks that connect the city’s surroundings, a sustainable mobility plan, and urban green structure strategies. Previous studies establish a connection to the importance of greening to mitigate the surface urban heat island (SUHI) and evaluate the effectiveness of these measures on urban climate. In this study, land surface temperature (LST), a remote sensing (RS) parameter, recorded by Landsat satellites (5, 7, and 8) was used to evaluate the effect of SUHI in Vitoria-Gasteiz between 1985–2021. The aim was to evaluate whether the urban greening actions influenced the local thermal conditions and, consequently, helped minimize the SUHI. Thirty sampling locations were identified, corresponding to different local climate zones (LCZ), at which LST data were extracted. A total of 218 images were processed and separated into summer and winter. Four of the 30 locations had, since 2003, on-site meteorological stations with regular air temperature (Tair) measurements which were used to validate the LST data. The results showed that Spearman’s correlation between Tair and LST was higher than 0.88 in all locations. An amount of 21 points maintained the same LCZ classification throughout the analysed period and nine underwent a LCZ transformation. The highest average temperature was identified in the city centre (urbanized area), and the lowest average was in a forest on the outskirts of the city. SUHI was more intense during the summer. A significant increase in SUHI intensity was identified in areas transformed from natural to urban LCZs. However, SUHI during satellite data acquisition periods has shown a minimal change in areas where sustainable practices have been implemented. RS was valuable for analysing the thermal behaviour of the LCZs, despite the limitation inherent in the satellite’s time of passage, in which the SUHI effect is not as evident.
Impact of Assimilating GK-2A All-Sky Radiance with a New Observation Error for Summer Precipitation Forecasting
Authors: Hastuti Miranti Indri , Min Ki-Hong
Published: June 2023

In the assimilation of all-sky radiance (ASR), the non-Gaussian behaviour of observation-minus-background (OMB) departures has been the major issue. Treating observation error properly should give the distribution OMB departures closer to Gaussian on which data assimilation systems are based. This study introduces a look-up-table (LUT) observation error inflation (LOEI) for assimilating ASR from three water vapor channels of GEO-KOMPSAT-2A (GK-2A) geostationary satellite based on a three-dimensional variational data assimilation (3DVAR) framework. The impacts are assessed based on summer precipitation cases over South Korea. To confirm all kinds of radiance observations, the ASRs are assimilated without any quality control procedures. The LOEI adopt a pre-estimated radiance error statistics by using the higher order fitting function of cloud amount (CA) and standard deviation (STD) of OMB departures. This LOEI was produced during the summer period from August 1 to 30, 2020, representing the characteristics of the atmosphere condition during the experimental period. The promising impact of LOEI is demonstrated in comparison with the inflated observation error using a simple linier function proposed by Geer and Bauer (GBOEI). Study results revealed the LOEI normalized OMB departures into much more Gaussian form than the GBOEI. Hence, the assimilation of ASR using LOEI (ExpLOEI) produced BT analysis closer to the observation in four cloud phases in contrast with ASR assimilation using GBOEI (ExpGBOEI), which obviously found in the ice phase. The better BT analysis eventually simulated more realistic moisture and temperature variables in the background field. Consequently, the ExpLOEI exhibited more accuracy in precipitation location and intensity compared to the experiment with ExpGBOEI.
Estimation of Forest Fire Burned Area by Distinguishing Non-Photosynthetic and Photosynthetic Vegetation Using Triangular Space Method
Authors: Wang Xiaoqiong , Yan Jun , Tian Qingjiu , Li Xianyi , Tian Jia , Zhu Cuicui , Li Qianjing
Published: June 2023

The forest fire burned area is one of the most basic factors used to describe forest fires and plays a vital role in damage assessment. The development of the NSSI-NDVI vegetation index triangular space method enables simultaneous calculation of the flammable non-photosynthetic vegetation (NPV), combustible photosynthetic vegetation (PV), and incombustible bare soil (BS) fractional cover in forest areas. This can be used to compensate for the calculation method that was based on NDVI vegetation index only by comparing vegetation cover before and after forest fires, with the omission of the NPV burned area. To this end, the NSSI-NDVI triangular space shape consistency before and after forest fires was elucidated through combustion and ash wetting experiments. In addition, the feasibility of the NSSI-NDVI triangular space method for the accurate calculation of the post-fire vegetation damage area was verified. Finally, the applicability and accuracy of this research method were verified based on 10 m spatial resolution satellite hyperspectral images from before and after the forest fire in Lushan, Sichuan Province, China. The NSSI-NDVI triangular space method was used to calculate the PV, NPV, and BS coverage simultaneously, and component transformation was used to calculate the burned area and burned site separately.
A SAR Image-Despeckling Method Based on HOSVD Using Tensor Patches
Authors: Fang Jing , Mao Taiyong , Bo Fuyu , Hao Bomeng , Zhang Nan , Hu Shaohai , Lu Wenfeng , Wang Xiaofeng
Published: June 2023

Coherent imaging systems, such as synthetic aperture radar (SAR), often suffer from granular speckle noise due to inherent defects, which can make interpretation challenging. Although numerous despeckling methods have been proposed in the past three decades, SAR image despeckling remains a challenging task. With the extensive use of non-local self-similarity, despeckling methods under the non-local framework have become increasingly mature. However, effectively utilizing patch similarities remains a key problem in SAR image despeckling. This paper proposes a three-dimensional (3D) SAR image despeckling method based on searching for similar patches and applying the high-order singular value decomposition (HOSVD) theory to better utilize the high-dimensional information of similar patches. Specifically, the proposed method extends two-dimensional (2D) to 3D for SAR image despeckling using tensor patches. A new, non-local similar patch-searching measure criterion is used to classify the patches, and similar patches are stacked into 3D tensors. Lastly, the iterative adaptive weighted tensor cyclic approximation is used for SAR image despeckling based on the HOSVD method. Experimental results demonstrate that the proposed method not only effectively reduces speckle noise but also preserves fine details.
A Novel Method of Ionospheric Inversion Based on Horizontal Constraint and Empirical Orthogonal Function
Authors: Wen Debao , Tang Yinghao , Xie Kangyou
Published: June 2023

Tomographic inversion of the ionosphere is a rank-deficient problem. To overcome the above problem, an algebraic reconstruction technique (ART) based on adaptive horizontal constraint and empirical orthogonal function (ARTHCEOF) is proposed. The new algorithm avoids the difficulty of vertically constrained matrix construction and resolves the description of the ionospheric vertical structure by using EOF. To confirm the feasibility and validate the ascendancy of the ARTHCEOF, three algorithms are first tested by using the emulated scheme. The test results show that the ARTHCEOF surpasses the ART and the ART based on the horizontal constraint (ARTHC) in both the inversion accuracy and computational efficiency. Finally, the ARTHCEOF method is applied to invert electron density values using the GNSS measurements during different geomagnetic activities. The tomographic images validate that the ARTHCEOF can reflect ionospheric daily changes in the European region. The altitudinal profiles in a fixed location are illustrated according to the inversion results of ARTHCEOF. Compared with the profiles recorded by the ionosonde station, the altitudinal profiles of ARTHCEOF have a good consistency. In the meantime, the VTEC values are inverted using the CIT results. The differential VTEC values are calculated by means of the inverted VTEC values and ionospheric products of CODE. The differential results further identify the dependability of ARTHCEOF.
ADD-UNet: An Adjacent Dual-Decoder UNet for SAR-to-Optical Translation
Authors: Luo Qingli , Li Hong , Chen Zhiyuan , Li Jian
Published: June 2023

Synthetic aperture radar (SAR) imagery has the advantages of all-day and all-weather observation. However, due to the imaging mechanism of microwaves, it is difficult for nonexperts to interpret SAR images. Transferring SAR imagery into optical imagery can better improve the interpretation of SAR data and support the further fusion research of multi-source remote sensing. Methods based on generative adversarial networks (GAN) have been proven to be effective in SAR-to-optical translation tasks. To further improve the translation results of SAR data, we propose a method of an adjacent dual-decoder UNet (ADD-UNet) based on conditional GAN (cGAN) for SAR-to-optical translation. The proposed network architecture adds an adjacent scale of the decoder to the UNet, and the multi-scale feature aggregation of the two decoders improves structures, details, and edge sharpness of generated images while introducing fewer parameters compared with UNet++. In addition, we combine multi-scale structure similarity (MS-SSIM) loss and L1 loss as loss functions with cGAN loss together to help preserve structures and details. The experimental results demonstrate the superiority of our method compared with several state-of-the-art methods.
Real-Time Magnetocardiography with Passive Miniaturized Coil Array in Earth Ambient Field
Authors: Zhu Keren , Kiourti Asimina
Published: June 2023

We demonstrate a magnetocardiography (MCG) sensor that operates in non-shielded environments, in real-time, and without the need for an accompanying device to identify the cardiac cycles for averaging. We further validate the sensor’s performance on human subjects. Our approach integrates seven (7) coils, previously optimized for maximum sensitivity, into a coil array. Based on Faraday’s law, magnetic flux from the heart is translated into voltage across the coils. By leveraging digital signal processing (DSP), namely, bandpass filtering and averaging across coils, MCG can be retrieved in real-time. Our coil array can monitor real-time human MCG with clear QRS complexes in non-shielded environments. Intra- and inter-subject variability tests confirm repeatability and accuracy comparable to gold-standard electrocardiography (ECG), viz., a cardiac cycle detection accuracy of >99.13% and averaged R-R interval accuracy of <5.8 ms. Our results confirm the feasibility of real-time R-peak detection using the MCG sensor, as well as the ability to retrieve the full MCG spectrum as based upon the averaging of cycles identified via the MCG sensor itself. This work provides new insights into the development of accessible, miniaturized, safe, and low-cost MCG tools.
Role-Driven Clustering of Stakeholders: A Study of IoT Security Improvement
Authors: Almalki Latifah , Alnahdi Amany , Albalawi Tahani
Published: June 2023

This study aims to address the challenges of managing the vast amount of data generated by Internet of Things (IoT) devices by categorizing stakeholders based on their roles in IoT security. As the number of connected devices increases, so do the associated security risks, highlighting the need for skilled stakeholders to mitigate these risks and prevent potential attacks. The study proposes a two-part approach, which involves clustering stakeholders according to their responsibilities and identifying relevant features. The main contribution of this research lies in enhancing decision-making processes within IoT security management. The proposed stakeholder categorization provides valuable insights into the diverse roles and responsibilities of stakeholders in IoT ecosystems, enabling a better understanding of their interrelationships. This categorization facilitates more effective decision making by considering the specific context and responsibilities of each stakeholder group. Additionally, the study introduces the concept of weighted decision making, incorporating factors such as role and importance. This approach enhances the decision-making process, enabling stakeholders to make more informed and context-aware decisions in the realm of IoT security management. The insights gained from this research have far-reaching implications. Not only will they benefit stakeholders involved in IoT security, but they will also assist policymakers and regulators in developing effective strategies to address the evolving challenges of IoT security.
A Wireless Sensor Network for Residential Building Energy and Indoor Environmental Quality Monitoring: Design, Instrumentation, Data Analysis and Feedback
Authors: Bourdeau Mathieu , Waeytens Julien , Aouani Nedia , Basset Philippe , Nefzaoui Elyes
Published: June 2023

This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building common areas and in apartments to monitor energy consumption, indoor environmental quality, and local meteorological conditions. The collected data are used and analyzed to assess the building performance in terms of energy consumption and indoor environmental quality following major renovation operations on the buildings. Observations from the collected data show energy consumption of the renovated buildings in agreement with expected energy savings calculated by an engineering office, many different occupancy patterns mainly related to the professional situation of the households, and seasonal variation in window opening rates. The monitoring was also able to detect some deficiencies in the energy management. Indeed, the data reveal the absence of time-of-day-dependent heating load control and higher than expected indoor temperatures because of a lack of occupant awareness on energy savings, thermal comfort, and the new technologies installed during the renovation such as thermostatic valves on the heaters. Lastly, we also provide feedback on the performed sensor network from the experiment design and choice of measured quantities to data communication, through the sensors’ technological choices, implementation, calibration, and maintenance.
Stepping beyond Counts in Recovery of Total Knee Arthroplasty: A Prospective Study on Passively Collected Gait Metrics
Authors: Fary Cam , Cholewa Jason , Abshagen Scott , Van Andel Dave , Ren Anna , Anderson Mike B. , Tripuraneni Krishna R.
Published: June 2023

Advances in algorithms developed from sensor-based technology data allow for the passive collection of qualitative gait metrics beyond step counts. The purpose of this study was to evaluate pre- and post-operative gait quality data to assess recovery following primary total knee arthroplasty. This was a multicenter, prospective cohort study. From 6 weeks pre-operative through to 24 weeks post-operative, 686 patients used a digital care management application to collect gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-samples t-test. Recovery was operationally defined as when the respective weekly average gait metric was no longer statistically different than pre-operative. Walking speed and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-operative (p < 0.0001). Walking speed recovered at 21 weeks (1.00 m/s, p = 0.063) and double support percentage recovered at week 24 (32%, p = 0.089). Asymmetry percentage was recovered at 13 weeks (14.0%, p = 0.23) and was consistently superior to pre-operative values at week 19 (11.1% vs. 12.5%, p < 0.001). Step length did not recover during the 24-week period (0.60 m vs. 0.59 m, p = 0.004); however, this difference is not likely clinically relevant. The data suggests that gait quality metrics are most negatively affected two weeks post-operatively, recover within the first 24-weeks following TKA, and follow a slower trajectory compared to previously reported step count recoveries. The ability to capture new objective measures of recovery is evident. As more gait quality data is accrued, physicians may be able to use passively collected gait quality data to help direct post-operative recovery using sensor-based care pathways.
Ultrasensitive Surface Plasmon Resonance Sensor with a Feature of Dynamically Tunable Sensitivity and High Figure of Merit for Cancer Detection
Authors: Gollapalli Ravi , Phillips Jonathan , Paul Puneet
Published: June 2023

Cancer is one of the leading causes of death worldwide, and it is well known that an early detection of cancer in a human body will provide an opportunity to cure the cancer. Early detection of cancer depends on the sensitivity of the measuring device and method, where the lowest detectable concentration of the cancerous cell in a test sample becomes a matter of high importance. Recently, Surface Plasmon Resonance (SPR) has proven to be a promising method to detect cancerous cells. The SPR method is based on the detection of changes in refractive indices of samples under testing and the sensitivity of such a SPR based sensor is related to the smallest detectable change in the refractive index of the sample. There exist many techniques where different combinations of metals, metal alloys and different configurations have been shown to lead to high sensitivities of the SPR sensors. Based on the difference in the refractive index between a normal healthy cell and a cancerous cell, recently, SPR method has been shown to be applicable to detect different types of cancers. In this work, we propose a new sensor surface configuration that comprises of gold-silver-graphene-black phosphorus to detect different cancerous cells based on the SPR method. Additionally, recently we proposed that the application of electric field across gold-graphene layers that form the SPR sensor surface can provide enhanced sensitivity than that is possible without the application of electrical bias. We utilized the same concept and numerically studied the impact of electrical bias across the gold-graphene layers combined with silver and black Phosphorus layers which forms the SPR sensor surface. Our numerical results have shown that electrical bias across the sensor surface in this new heterostructure can provide enhanced sensitivity compared to the original unbiased sensor surface. Not only that, our results have shown that as the electrical bias increases, the sensitivity increases up to a certain value and stabilizes at a still improved sensitivity value. Such dependence of sensitivity on the applied bias provides a dynamic tunability of the sensitivity and figure-of-merit (FOM) of the sensor to detect different types of cancer. In this work, we used the proposed heterostructure to detect six different types of cancers: Basal, Hela, Jurkat, PC12, MDA-MB-231, and MCF-7. Comparing our results to work published recently, we were able to achieve an enhanced sensitivity ranging from 97.2 to 1851.4 (deg/RIU) and FOM values ranging from 62.13 to 89.81 far above the values presented recently by other researchers.
Analysis of Absorber Packed Height for Power Plants with Post-Combustion CO2 Capture
Authors: Navarrete Procopio Miriam , Urquiza Gustavo , Castro Laura
Published: June 2023

The electricity generation process from fossil fuels is one of the sources of CO2 emissions. The post-combustion CO2 capture is an alternative to minimize emissions. The packed absorption column is the first unit of the CO2 capture process. In this study, the values of the process parameters were established to reduce the absorber-packed height using a simulator developed in this work. The simulator was validated using measurements in a laboratory-scale absorption unit; simulations were carried out with the same operating conditions as measurements and two different fuels were treated; coal and natural gas. A combined-cycle power plant in Mexico was simulated, with the objective of evaluating the main parameters in the absorption process and required dimensions of the packed absorption column required to carry out the capture of CO2 in the power plant. From the result of the simulations, three columns treatment with 3 m diameter and 7 m height were established to remove 99% of the CO2 of the flue gases with 20 wt.% of MEA composition using Mellapak 500Y structured packaging.
Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation
Authors: Wei Wei , Ye Li , Fang Yi , Wang Yingchun , Chen Xi , Li Zhenhua
Published: June 2023

The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the deployment of energy storage in the microgrid. To this end, a typical multi-day scenario set is used as the simulation operation scenario, and an optimal allocation method of microgrid energy storage capacity considering the uncertainty of renewable energy generation is designed. Firstly, the historical scenarios are clustered into K types of daily state types using the K-means algorithm, and the corresponding probability distribution is obtained. Secondly, the Latin hypercube sampling method is used to obtain the state type of each day in a multi-day scenario set. Then, the daily scenario generation method based on conditional generative adversarial networks is used to generate a multi-day scenario set, combining the day state type as a condition, and then the typical scenario set is obtained using scenario reduction. Furthermore, a double-layer optimization allocation model for the energy storage capacity of microgrids is constructed, in which the upper layer optimizes the energy storage allocation capacity and the lower layer optimizes the operation plans of microgrids in each typical scenario. Finally, the proposed model is solved using the PSO algorithm nested with the CPLEX solver. In the microgrid example, the proposed method reduces the expected annual total cost by 19.66% compared with the stochastic optimal allocation method that assumes the scenic power obeys a specific distribution, proving that it can better cope with the uncertainty of renewable energy generation. At the same time, the expected annual total cost is reduced by 6.99% compared with the optimal allocation method that generates typical daily scenarios based on generative adversarial networks, which proves that it can better cope with the high dimensionality of renewable energy generation.
Study on Mechanical Calculation Model of Arch Ring in Freestanding Stone Cave-Dwelling
Authors: Hao Yan’e , Lan Yongqiang
Published: June 2023

The freestanding stone cave-dwelling is a kind of arched and sheltered house built with stones in the Loess Plateau region of northwest China. Amazingly, before construction, this kind of cave-dwelling was not formally calculated and designed in theory, but only built on the experience passed down by predecessors. The arch ring is the main load-bearing component of the freestanding stone cave-dwelling, through which the upper loads are transmitted to the legs on the left and right sides of the cave-dwelling and then to the foundations. Therefore, it is a prerequisite to ensure the safety of cave dwellings by adopting a reasonable and accurate mechanical calculation model for the arch ring of a stone cave-dwelling to reveal the distribution of internal forces in the arch ring and scientifically design the arch ring accordingly. Three mechanical calculation models (structural calculation diagrams) are adopted for the arch ring of stone cave-dwelling, namely, hingeless arch, two-hinged arch, and three-hinged arch. Based on the force equilibrium and the force method from the structure mechanics, the formulae for calculating the internal force of the stone arch ring under these three different mechanical calculation models are derived respectively. The mechanical calculation results of three calculation models are compared and analyzed to clarify the difference and rationality of the stress results of the arch ring under the three mechanical calculation models and the degree of influence on the force of the lower cave-dwelling leg members. Lastly, in accordance with the internal force calculation results, the calculation formulae for the design of the arch ring thickness are proposed. The study shows that the hingeless arch and two-hinged arch models are more consistent with the actual failure characteristics, two-hinged arch calculation model is safer, more accu-rate, and more reliable than the hingeless arch calculation model when it is used in the mechanical analysis of circular arc arch ring. The findings are intended to serve as theoretical references for the design and construction, protection and reinforcement, and sustainable development and inheritance of cave dwellings in the future.
Enhancing Resilience in Coastal Regions from a Socio-Ecological Perspective: A Case Study of Andhra Pradesh, India
Authors: Dakey Shruthi , Deshkar Sameer , Joshi Shreya , Sukhwani Vibhas
Published: June 2023

Addressing the second priority of the Sendai Framework on Disaster Risk Reduction (SFDRR) for strengthening disaster risk governance, this paper considers coastal regions from a Socio-Ecological System (SES) perspective. It identifies the drivers of risk and critical linkages between various key components of coastal SESs that can potentially influence the resilience of coastal regions. The study considers that, in a coastal area, where the system is complex with numerous stakeholders, any decision taken in the form of developmental policy or as an adaptive practice is critical for system changes, thereby making risk anticipation and its management difficult. To further conduct a detailed investigation of this subject, seven settlements along the coastal Andhra Pradesh (India) region were methodically selected. Herein, focus group discussions were conducted to prepare fuzzy cognitive maps (FCM), which were further analyzed through the mental modeler online platform. By analyzing the structural metrics of the FCM, saltwater intrusion was identified as a significant system component. Furthermore, the study also analyzed the effects of change on the characteristics of selected SES components regarding the effectiveness of existing governance strategies and, conversely, the impact of implementing governance strategies on the identified critical components of SES. By critically examining the various scenarios thus generated, the research identifies various components, generating vulnerabilities in human systems. The derived results were cross-validated through community consultation, and the temporal variations in the state of system components were mapped. The study is expected to provide considerable input to policymakers for enhancing decision-making in coastal regions from an SES perspective.
Influence of Dockless Shared E-Scooters on Urban Mobility: WTP and Modal Shift
Authors: Glavić Draženko , Milenković Marina , Trifunović Aleksandar , Jokanović Igor , Komarica Jelica
Published: June 2023

Land use largely depends on the traffic policy of a city. The appearance of e-scooters can greatly affect the visual distribution of transportation, and thus the occupation of land, primarily in the central areas of cities. E-scooters as a shared micro-mobility service have become widespread worldwide since 2017. The advent of e-scooters has made changes in travel habits, especially in the central parts of big cities. However, many issues are focused on e-scooter shared mobility management policies. One of the important issues is the price of renting an e-scooter, on which the percentage of users who use e-scooters largely depend. In order to determine willingness to pay for e-scooter dockless shared mobility, a survey was conducted in the city of Belgrade (Serbia, Europe) on the willingness of participants to use this mode of transport for commuting and other travel purposes depending on the price of renting an e-scooter. The results showed that price plays an important role in the willingness of participants to use an e-scooter. The paper presents mathematical models, which include the cost of renting an e-scooter and the percentage of participants who would accept this type of transport. These mathematical models can help a decision maker to determine the pricing policy in order to maximize the profit from renting an e-scooter, as well as to influence modal shift in order to reduce car-dependent trips.
Data-Driven Sustainable Supply Chain Decision Making in the Presence of Low Carbon Awareness
Authors: Qiao Xiaojiao , Xu Shimeng , Shi Dan , Zhao Xiukun
Published: June 2023

Low-carbon production is a vital solution for many environmental problems, as is consumers’ consciousness about carbon playing a more important role and ultimately passing upstream. Supply chains are eager to seek sustainable development via appropriate decision making with data-driven methods. Consistent with this aim, we investigated decisions toward lower carbon efforts and prices in a two-echelon supply chain via a game theoretical approach. The decision-making scenarios of decentralized, centralized, and cost-sharing contracts were investigated and compared. The results show that the level of improvement in environmental performance is positively correlated with the degree of cooperation between partners. Cooperation between partners would be even more significant with an increase in consumers’ low carbon awareness. Furthermore, cost-sharing contracts improve the performance of the entire supply chain compared with decentralized cases. Finally, we implemented numerical experiments to verify the modeling results. Therefore, this study provides theoretical support toward sustainable operations for supply chains concerning low carbon awareness.
Research on Combination of Distributed Generation Placement and Dynamic Distribution Network Reconfiguration Based on MIBWOA
Authors: Yan Xin , Zhang Qian
Published: June 2023

This paper aims to address the combination of distributed generation placement and dynamic distribution network reconfiguration. Herein, a multi-strategy multi-objective improved black widow algorithm is proposed. A model is established, which considers the objectives of minimizing active power loss, voltage deviation, and carbon emission. The proposed algorithm significantly enhances the traversal capability and search speed by employing Cubic–Tent chaotic mapping, involving a novel formula with the fusion of optimal genes, and employing an adaptive mutation of Wald mutation and elite reverse learning mixing. The DeepSCN is employed to forecast the distributed generation (DG) output power and distribution network load. Through various test functions, the capability of the proposed algorithm is demonstrated. Whether single-objective or multi-objective, the algorithm has excellent performance. To showcase the practicality and effectiveness of the model and approach, a simulation experiment was performed on the IEEE-33 node configuration. The solution set provided by MIBWOA can reduce active network loss to improve operating efficiency, increase voltage offset to make operation more stable, and reduce carbon emissions to make operation more environmentally friendly. The proposed algorithm shows excellent performance in distributed generation placement and distribution network reconfiguration compared with the comparison algorithms. The results show that the solution proposed by MIBWOA can enhance the real-time operational parameters of the distribution network with considerable efficiency.
Innovative Energy-Efficient Prefabricated Movable Buildings for Smart/Co-Working: Performance Assessment upon Varying Building Configurations
Authors: Maffei Luigi , Ciervo Antonio , Perrotta Achille , Masullo Massimiliano , Rosato Antonio
Published: June 2023

Worldwide, smart/co-working spaces are growing significantly, and prefabricated movable buildings for such an application could (i) save energy, CO2 emissions, and costs; (ii) enhance the worker’s perceived sense of surroundings; and (iii) support the rebirth of small villages with high regenerative potential. Innovative prefabricated movable building configurations to be used as an office for smart/co-working by a maximum of 6 persons have been designed and analyzed based on simulation data. In particular, 10 case studies corresponding to building configurations differing in terms of innovative energy-efficient measures related to the building envelope (smart windows operated under various control logics) and the energy systems serving the building (photovoltaic panels, small wind turbines, and electric storages) have been modeled and simulated by applying detailed dynamic simulation models via the simulation software TRNSYS. The performance of the 10 case studies has been compared from energy, environmental, and economic points of view with respect to a baseline system characterized by conventional building envelope and energy systems, with the aim of assessing the proposed measures and identifying the most efficient configuration. The simulation results highlighted that: (i) all the proposed alternative configurations allow to save primary energy (from 10.3% up to 100%), equivalent CO2 emissions(from 10.3% up to 100%), and operating costs (from 8.5% up to 100%) with respect to the baseline building; (ii) the building configurations including the smart windows only are not economically feasible in terms of simple pay-back (SPB) period, while the building configurations equipped with photovoltaic panels and/or electric storages and/or wind turbine represent a suitable investment thanks to an SPB lower than 15.2 years; (iii) a stand-alone building configuration for smart/co-working with energy demands totally covered by means of renewable sources can be obtained by combining smart windows, photovoltaic panels, electric storages and wind turbine.