bluuebunny/crossref_metadata_2025_split huggingface dataset:
Datasets of a Multizone Office Building under Different HVAC System Operation Scenarios
Authors: Yoon Yeobeom , Jung Sungkyun , Im Piljae , Gehl Anthony 
Published: December 2022

AbstractThis study provides an open-source dataset of the measured weather data, building indoor data, and system data under the different test settings. The test building is the two-story Flexible Research Platform building at the US Department of Energy’s Oak Ridge National Laboratory, in Oak Ridge, Tennessee. Four heating tests and three cooling tests were conducted. The 1-min interval of weather, building indoor data, and system data from each test setting are provided. Actual weather data were collected from a weather station installed on the roof. This paper describes information on the test building and installed sensors, data collection method, and data validation. The provided dataset can be employed to understand HVAC system conditions and building indoor conditions under different HVAC system operations and the performance of building envelope without HVAC system operation using free-floating test data. Additionally, it can be used for empirical validation of the building energy modelling engine.
Global patterns of prognostic biomarkers across disease space
Authors: Murad Neha , Melamud Eugene 
Published: December 2022

AbstractThere is a multitude of pathological conditions that affect human health, yet we currently lack a predictive model for most diseases, and underlying mechanisms that are shared by multiple diseases are poorly understood. We leveraged baseline clinical biomarker data and long-term disease outcomes in UK Biobank to build prognostic multivariate survival models for over 200 most common diseases. We construct a similarity map between biomarker-disease hazard ratios and demonstrate broad patterns of shared similarity in biomarker profiles across the entire disease space. Further aggregation of risk profiles through density based clustering showed that biomarker-risk profiles can be partitioned into few distinct clusters with characteristic patterns representative of broad disease categories. To confirm these risk patterns we built disease co-occurrence networks in the UK Biobank and US HCUP hospitalization databases, and compared similarity in biomarker risk profiles to disease co-occurrence. We show that proximity in the biomarker-disease space is strongly related to the occurrence of disease comorbidity, suggesting biomarker profile patterns can be used for both predicting future outcomes as well as a sensitive mechanism for detecting under-diagnosed disease states.
Metabolomics analysis of plasma samples of patients with fibromyalgia and electromagnetic sensitivity using GC–MS technique
Authors: Piras Cristina , Pibiri Monica , Conte Stella , Ferranti Gabriella , Leoni Vera Piera , Liggi Sonia , Spada Martina , Muntoni Sandro , Caboni Pierluigi , Atzori Luigi 
Published: December 2022

AbstractFibromyalgia (FM) is a chronic and systemic condition that causes widespread chronic pain, asthenia, and muscle stiffness, as well as in some cases depression, anxiety, and disorders of the autonomic system. The exact causes that lead to the development of FM are still unknown today. In a percentage of individuals, the symptoms of FM are often triggered and/or exacerbated by proximity to electrical and electromagnetic devices. Plasma metabolomic profile of 54 patients with fibromyalgia and self-reported electromagnetic sensitivity (IEI-EMF) were compared to 23 healthy subjects using gas chromatography-mass spectrometry (GC–MS) coupled with multivariate statistical analysis techniques. Before the GC–MS analysis the plasma samples were extracted with a modified Folch method and then derivatized with methoxamine hydrochloride in pyridine solution and N-trimethylsilyltrifuoroacetamide. The combined analysis allowed to identify a metabolomic profile able of distinguishing IEI-EMF patients and healthy subjects. IEI-EMF patients were therefore characterized by the alteration of 19 metabolites involved in different metabolic pathways such as energy metabolism, muscle, and pathways related to oxidative stress defense and chronic pain. The results obtained in this study complete the metabolomic "picture" previously investigated on the same cohort of IEI-EMF patients with1H-NMR spectroscopy, placing a further piece for better understanding the pathophysiological mechanisms in patients with IEI-EMF.
Diagnosis of intracranial lesions using accelerated 3D T1 MPRAGE with wave-CAIPI technique: comparison with conventional 3D T1 MPRAGE
Authors: Lee Eun Jung , Kim Min Gu , Chung Mi Sun , Kim Seon-Ok , Byun Jun Soo , Yim Younghee 
Published: December 2022

AbstractWe aimed to evaluate the agreement in the diagnosis of intracranial lesions between conventional pre-contrast 3D T1 magnetization-prepared rapid gradient echo (MPRAGE) and wave-CAIPI (wave-controlled aliasing in parallel imaging) MPRAGE. Institutional review board approval was obtained and informed consent was waived for this retrospective study. We included 149 consecutive patients who had undergone brain MR with both conventional MPRAGE (scan time: 5 min 42 s) and wave-CAIPI MPRAGE (scan time: 2 min 44 s) from February to June 2018. All images were independently reviewed by two radiologists for the diagnosis of intracranial lesion and scored image quality using visual analysis. One technician measured signal-to-noise ratio. The agreement for diagnosis of intracranial lesion was calculated, and the intra- and interobserver agreements were analyzed by using kappa value. For the diagnosis of intracranial lesion, the conventional and wave-CAIPI MPRAGE demonstrated 99.7% of agreement (297 of 298) in the pooled analysis with very good agreement (k = 0.994). Intra- and inter-observer agreement showed very good (k > 0.9 in all) and good (k > 0.75) agreement, respectively. In the quantitative analysis, the signal-to-noise ratio had no difference (P > 0.05 for all). The overall image quality was poorer in images of wave-CAIPI MPRAGE (P < 0.001), but motion artifact had no difference between two sequences (P = 0.06). Compared to conventional MPRAGE, pre-contrast 3D T1 wave-CAIPI MPRAGE achieved higher agreement for the diagnosis of intracranial lesions and reduced the scan time by approximately 50%.
Explainable machine learning framework for predicting long-term cardiovascular disease risk among adolescents
Authors: Salah Haya , Srinivas Sharan 
Published: December 2022

AbstractAlthough cardiovascular disease (CVD) is the leading cause of death worldwide, over 80% of it is preventable through early intervention and lifestyle changes. Most cases of CVD are detected in adulthood, but the risk factors leading to CVD begin at a younger age. This research is the first to develop an explainable machine learning (ML)-based framework for long-term CVD risk prediction (low vs. high) among adolescents. This study uses longitudinal data from a nationally representative sample of individuals who participated in the Add Health study. A total of 14,083 participants who completed relevant survey questionnaires and health tests from adolescence to young adulthood were chosen. Four ML classifiers [decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN)] and 36 adolescent predictors are used to predict adulthood CVD risk. While all ML models demonstrated good prediction capability, XGBoost achieved the best performance (AUC-ROC: 84.5% and AUC-PR: 96.9% on testing data). Besides, critical predictors of long-term CVD risk and its impact on risk prediction are obtained using an explainable technique for interpreting ML predictions. The results suggest that ML can be employed to detect adulthood CVD very early in life, and such an approach may facilitate primordial prevention and personalized intervention.
Design of ionic liquids containing glucose and choline as drug carriers, finding the link between QM and MD studies
Authors: Kalhor Sepideh , Fattahi Alireza 
Published: December 2022

AbstractDesigning drug delivery systems for therapeutic compounds whose receptors are located in the cytosol of cells is challenging as a bilayer cell membrane is negatively charged. The newly designed drug delivery systems should assist the mentioned drugs in passing the membrane barriers and achieving their targets. This study concentrated on developing novel ionic liquids (ILs) that interact effectively with cell membranes. These ILs are based on glucose-containing choline and are expected to be non-toxic. The binding energies of the known pharmaceutically active ionic liquids were calculated at the B3LYP/6-311++G(d,p) level in the gas phase and compared with those of our newly designed carbohydrate-based ionic liquids. Subsequently, we employed MD simulations to obtain information about the interactions of these known and designed ILs with the cell membrane. In our approach, we adopted QM and MD studies and illustrated that there could be a link between the QM and MD results.
Corporate social responsibility, debt financing cost and enterprise innovation
Authors: Yao Jing-jing , Qi Yi-ang , Guo Bing 
Published: December 2022

AbstractBased on the data of A-share listed companies in China from 2016 to 2020, this study empirically analyzes the relationship among corporate social responsibility, debt financing cost and enterprise innovation by constructing a regulated mediation effect model. The results show that enterprises perform social responsibility actively can enhance the level of enterprise innovation. Besides, debt financing cost plays a part of the intermediary role between corporate social responsibility and enterprise innovation. It is found that market competition degree positively regulates the relationship between corporate social responsibility and enterprise innovation, and market competition degree strengthens the part of the intermediary effect of debt financing cost. The conclusions not only help to reveal the impact mechanism of corporate social responsibility on enterprise innovation, but also provide empirical evidence for promoting enterprises to actively assume social responsibility, improve the level of innovation, and provide empirical evidence for the government to formulate corresponding policies according to the degree of competition in different markets.
Blood glucose sensing by back gated transistor strips sensitized by CuO hollow spheres and rGO
Authors: Farahmandpour Milad , Haghshenas Hassan , Kordrostami Zoheir 
Published: December 2022

AbstractIn this work, a highly sensitive flexible glucose sensor based on a field effect transistor (FET) has been fabricated. It is shown that the proposed flexible transistor can be used as new non-enzymatic blood glucose test strips. CuO hollow-spheres decorated with reduced graphene oxide have been synthesized using the hydrothermal method. The shells of the hollow micro-spheres are formed by nanostructures. The synthesized nanostructured hollow micro-spheres (rGO/CuO–NHS) are deposited on a flexible PET substrate between interdigitated electrodes as the channel of a back gate transistor. The channel concentration and the FET bias are optimized so that the sensor exhibits extremely low limit of detection and high sensitivity. The combination of selective porous CuO hollow spheres and the high surface to volume ratio of their nanostructured shells with the high mobility and high conductivity rGO led to faster and higher charge-transfer capability and superior electro-catalyst activity for glucose oxidation. The glucose-dependent electrical responses of the sensor is measured in both resistive and transistor action modes. The amplification of the current by the induced electric field of the gate in the proposed FET-based biosensor provides advantages such as higher sensitivity and lower limit of detection compared to the resistive sensor. The flexible glucose sensor has a sensitivity of 600 μA μM−1and a limit of detection of 1 nM with high reproducibility, good stability, and highly selectivity. The high accuracy response of the biosensor towards the real blood serum samples showed that it can be used as a test strip for glucose detection in real blood samples.
Avatar led interventions in the Metaverse reveal that interpersonal effectiveness can be measured, predicted, and improved
Authors: Nagendran Arjun , Compton Scott , Follette William C. , Golenchenko Artem , Compton Anna , Grizou Jonathan 
Published: December 2022

AbstractExperiential learning has been known to be an engaging and effective modality for personal and professional development. The Metaverse provides ample opportunities for the creation of environments in which such experiential learning can occur. In this work, we introduce a novel interpersonal effectiveness improvement framework (ELAINE) that combines Artificial Intelligence and Virtual Reality to create a highly immersive and efficient learning experience using avatars. We present findings from a study that uses this framework to measure and improve the interpersonal effectiveness of individuals interacting with an avatar. Results reveal that individuals with deficits in their interpersonal effectiveness show a significant improvement (p < 0.02) after multiple interactions with an avatar. The results also reveal that individuals interact naturally with avatars within this framework, and exhibit similar behavioral traits as they would in the real world. We use this as a basis to analyze the underlying audio and video data streams of individuals during these interactions. We extract relevant features from these data and present a machine-learning based approach to predict interpersonal effectiveness during human-avatar conversation. We conclude by discussing the implications of these findings to build beneficial applications for the real world.
Effect of external isometric hip rotation force on lower extremity muscles activities during pelvic drop with different hip positions
Authors: piran Roghayeh Jalil , Babakhani Farideh , Balouchi Ramin , Hatefi Mohamadreza 
Published: December 2022

AbstractGluteus medius muscle (Gmed) dysfunction has been confirmed as a functional defect in subjects with Genu Valgum Deformity (GVD). The purpose of this study was to determine whether the change in the positions of hip rotation and applying isometric hip external rotation during pelvic drop (PD) can affect muscles activity in subjects with GVD. A total of thirty recreational female athletes with (n = 15) and without (n = 15) GVD participated in this study. Surface electromyography measured Gmed, tensor fascia latae (TFL), and quadratus lumborum (QL) muscles activity when subjects performed PD in three different positions of hip rotations with and without applied isometric hip external rotation force. There were differences in muscle activity between GVD and healthy subjects. The Gmed/TFL and Gmed/QL muscles activity ratio altered while placing the hip in different rotation positions and applying isometric load. The lower extremity muscles’ activity is affected by GVD, and changing the positions of the hip rotation in the PD task can be associated with altered muscle activity in both GVD and healthy Groups. However, applying isometric hip external rotation during PD can be suggested as an effective intervention to increase Gmed activity.
Streptozotocin-induced Alzheimer's disease investigation by one-dimensional plasmonic grating chip
Authors: Kadhim Hussam Jawad , Al-Mumen Haider , Nahi H. H. , Hamidi S. M. 
Published: December 2022

AbstractRecently, there has been significant interest in researching brain insulin resistance as it has been hypothesized that it may play a role in the progression of Alzheimer's disease. Alzheimer’s disease (AD) is brain dementia that contributes to damage to the neuron cells and then patient death. This dementia is ranked as the fifth more dangerous disease in the world. Streptozotocin (STZ) is used to induce Alzheimer’s disease experimentally. STZ is toxic to the pancreatic beta cells and induces insulin resistance. Neuroplasmonin techniques have been used to investigate the ability of STZ on the activity of cultured neuron cells. Neuroplasmonic is a novel technology that combines nanotechnology and biosensor. This technique has been used to record neuron signals in vivo and in vitro. Also, it has many facilities such as label-free detection, real-time analysis, biological compatibility, small sample, high throughput, and low detection limit. In this paper, we introduce a one-dimensional electro-plasmonic nanograting platform that consists of a straight nanorod of gold embedded in a dielectric layer of polycarbonate. The chip is connected with an externally applied voltage to induce tunable PIT and increase the sensor sensitivity. To evaluate the sensing performance of the electro-plasmonic sensor, this chip was cultured with Human Nucleus Pulposus Cells (HNPC). The first step was to measure the neuron cell activity in a healthy case. The next step was to measure the activity of neuron cells injected with different concentrations of STZ (0.5, 1, 2 mM) to induce the formation of Alzheimer’s disease in the cultured neuron cells. The results indicated that the electro-plasmonics sensor had a high sensitivity to the cells' activity and showed good results for the effecting STZ on the neuron cell’s activities.
Effects of ligand tuning and core doping of atomically precise copper nanoclusters on CO2 electroreduction selectivity
Authors: Ding Mei , Tang Li , Ma Xiaoshuang , Song Caixia , Wang Shuxin 
Published: December 2022

AbstractAtomically precise nanoclusters (NCs) provide opportunities for correlating the structure and electrocatalytic properties at atomic level. Herein, we report the single-atom doping effect and ligand effect on CO2electroreduction (eCO2RR) by comparing monogold-doped Au1Cu24and homocopper Cu25NCs protected by triphenylphosphine or/and tris(4-fluorophenyl)phosphine. Catalytic results revealed that the electronic distribution of Cu25NCs is enormously contracted by doping Au atoms, entitling it to exhibit the unique inhibition of hydrogen evolution reaction. And the inductive effect of ligand strongly favors the formation of formate in eCO2RR. Overall, this work will provide guidance for the rational design of the copper-based catalysts in the eCO2RR.
Performance comparison of successive interference cancellation decoding order for base station association and power control in small cell networks incorporating handoff optimization
Authors: Pirnia Sina , Bakhshi Hamidreza , Dosaranian‐Moghadam Mohamad , Khosravi Ramin 
Published: December 2022

AbstractThe co‐channel interference and frequent handoff are two important issues that have a great impact on the spectrum and energy efficiency in the non‐orthogonal multiple access (NOMA) with successive interference cancellation (SIC) in the small cell networks (SCN). This paper investigates the performance of SIC decoding order based on the users’ channel state information (CSI) and random SIC decoding order under the perfect and imperfect conditions for the handoff optimization in SCNs with NOMA. The effect of SIC decoding order in the joint base station association and power control problem is studied by considering the motion of mobile users (MUs). Two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs, under the maximum allowable transmit power and minimum average‐rate constraints. Two objectives are combined into a single‐stage optimization problem through the weighted sum method. The formulated problem is solved using a game theory‐based algorithm and primal decomposition theory. The simulation results show that changing the SIC decoding order can lead to performance improvement in the uplink NOMA‐ based SCNs. Also, it is shown that the proposed algorithm can significantly reduce the frequent handoffs in small cell networks.
Analysis of a spoke‐type ferrite PM motor with assisted poles based on equivalent magnetic network with hybrid cross‐shaped mesh elements
Authors: Ke Hao , Liu Zhengmeng , Chen Qian , Liu Guohai , Xu Gaohong 
Published: December 2022

AbstractThis article proposes an equivalent magnetic network (EMN) model to improve the accuracy of electromagnetic analysis, where a modular modelling of hybrid cross‐shaped mesh (CS‐M) elements is introduced. By applying hybrid CS‐M elements, the modelling of air gap and its surrounding regions is regulated into an identical width. As a result, the relative motion of the motor is simpler to simulate by changing the nodal connection without involving the non‐linear permeance. The airgap flux density is solved more accurately than the regular CS‐M elements as well. A 12‐slot/10‐pole ferrite permanent magnet (FPM) motor with assisted poles of unequal pole‐arc ratios is selected as an example to be built with the proposed EMN model. After solving the EMN model, the effectiveness and accuracy of the proposed EMN model are verified by comparing it with the FEA model and experiments. Additionally, based on the frozen permeability (FP) method, transient torque components can be separated accurately with a transient torque solution related to virtual work principle (VWP) for the EMN model.
Decentralized risk‐based security‐constrained optimal power flow in interconnected multi‐carrier microgrids
Authors: Sajjadi Kalajahi Seyed Mohammad , Seyedi Heresh , Tohidi Sajjad , Mohammadi‐Ivatloo Behnam 
Published: December 2022

AbstractThe security of energy systems has always been one of the most important issues of energy systems operation. The multi‐carrier multi‐microgrid (MCMMG) is one of the novel structures in the power system. High penetration of renewable resources and their uncertainty, their ability to operate in isolation mode, and the presence of energy hubs in MCMMG systems increase the importance of security assessment in this type of system. This paper presents a decentralized risk‐based security‐constrained optimal power flow model for an MCMMG system. The stochastic nature of wind energy, photovoltaic generation, and electrical and heating loads are considered in the proposed model. In the model, the risk level of the system is determined by considering both the electrical and natural gas (NG) networks’ contingencies. The proposed model is decomposed into three types of subproblems with the augmented Lagrangian relaxation (ALR) method: The subproblem related to the multi‐microgrid operator level, the NG network subproblem, and the individual microgrid level subproblem. The decomposed problem is solved using MATLAB software. According to the results, the proposed model can secure the system from high costs that may impose on the system due to the contingency occurrence.
Withdrawn: A computerized doughty predictor framework for corona virus disease: Combined deep learning based approach
Authors: Ramya P. , Babu S.V. 
Published: December 2022

AbstractThe above article, published online on 5 December 2022, on Wiley Online Library (https://onlinelibrary.wiley.com/doi/abs/10.1049/ipr2.12554), has been withdrawn by agreement between the authors, the journal Editor in Chief, Farzin Deravi, and John Wiley & Sons Ltd. The withdrawal has been agreed due to a technical error at the publisher that caused the article to be mistakenly published online although its publication had been cancelled at the request of the authors.