bluuebunny/crossref_metadata_2025 huggingface dataset:
Driver Identification Using Optimized Deep Learning Model in Smart Transportation
Authors: Ravi Chandrasekar , Tigga Anmol , Reddy G. Thippa , Hakak Saqib , Alazab Mamoun
Published: February 2022

The
Intelligent Transportation System (ITS)
is said to revolutionize the travel experience by making it safe, secure, and comfortable for the people. Although vehicles have been automated up to a certain extent, it still has critical security issues that require thorough study and advanced solutions. The security vulnerabilities of ITS allows the attacker to steal the vehicle. Therefore, the identification of drivers is required in order to develop a safe and secure system so that the vehicles can be protected from theft. There are two ways in which a driver can be identified: 1) face recognition of the driver, and 2) based on driving behavior. Face recognition includes image processing of 2-D images and learning of the features, which require high computational power. Drivers are known to have unique driving styles, whose data can be captured by the sensors. Therefore, the second method identifies drivers based on the analysis of the sensor data and it requires comparatively lesser computational power. In this paper, an optimized deep learning model is trained on the sensor data to correctly identify the drivers. The
Long Short-Term Memory (LSTM)
deep learning model is optimized for better performance. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The CAN-BUS dataset is used for experimentation and evaluation of the training model. Evaluation parameters such as accuracy, precision score, F1 score, and ROC AUC curve are considered to evaluate the performance of the model.
A Survey on Security of Digital Microfluidic Biochips: Technology, Attack, and Defense
Authors: Guo Wenzhong , Lian Sihuang , Dong Chen , Chen Zhenyi , Huang Xing
Published: February 2022

As an emerging lab-on-a-chip technology platform, digital microfluidic biochips (DMFBs) have been widely used for executing various laboratory procedures in biochemistry and biomedicine such as gene sequencing and near-patient diagnosis, with the advantages of low reagent consumption, high precision, and miniaturization and integration. With the ongoing rapid deployment of DMFBs, however, these devices are now facing serious and complicated security challenges that not only damage their functional integrity but also affect their system reliability. In this article, we present a systematic review of DMFB security, focusing on both the state-of-the-art attack and defense techniques. First, the overall security situation, the working principle, and the corresponding fabrication technology of DMFBs are introduced. Afterwards, existing attack approaches are divided into several categories and discussed in detail, including denial of service, intellectual property piracy, bioassay tampering, layout modification, actuation sequence tampering, concentration altering, parameter modification, reading forgery, and information leakage. To prevent biochips from being damaged by these attack behaviors, a number of defense measures have been proposed in recent years. Accordingly, we further classify these techniques into three categories according to their respective defense purposes, including confidentiality protection, integrity protection, and availability protection. These measures, to varying degrees, can provide effective protection for DMFBs. Finally, key trends and directions for future research that are related to the security of DMFBs are discussed from several aspects, e.g., manufacturing materials, biochip structure, and usage environment, thus providing new ideas for future biochip protection.
Learning from the Past: Efficient High-level Synthesis Design Space Exploration for FPGAs
Authors: Wang Zi , Schafer Benjamin Carrion
Published: February 2022

The quest to democratize the use of Field-Programmable Gate Arrays (FPGAs) has given High-Level Synthesis (HLS) the final push to be widely accepted with FPGA vendors strongly supporting this VLSI design methodology to expand the FPGA user base. HLS takes as input an untimed behavioral description and generates efficient RTL (Verilog or VHDL). One major advantage of HLS is that it allows us to generate a variety of different micro-architectures from the same behavioral description by simply specifying different combination of synthesis options. In particular, commercial HLS tools make extensive use of synthesize directives in the form pragmas. This strength is also a weakness as it forces HLS users to fully understand how these synthesis options work and how they interact to efficiently set them to get a hardware implementation with the desired characteristics. Luckily, this process can be automated. Unfortunately, the search space grows supra-linearly with the number of synthesis options. To address this, this work proposes an automatic synthesis option tuner dedicated for FPGAs. We have explored a larger number of behavioral descriptions targeting ASICs and FPGAs and found out that due to the internal structure of the FPGA a large number of synthesis options combinations never lead to a Pareto-optimal design and, hence, the search space can be drastically reduced. Moreover, we make use of large database of DSE results that we have generated since we started working in this field to further accelerate the exploration process. For this, we use a technique based on perceptual hashing that allows our proposed explorer to recognize similar program structures in the new description to be explored and match them with structures in our database. This allows us to directly retrieve the pragma settings that lead to Pareto-optimal configurations. Experimental results show that the search space can be accelerated substantially while leading to finding most of the Pareto-optimal designs.
A Common Terminology for Software Risk Management
Authors: Masso Jhon , García Félix , Pardo César , Pino Francisco J. , Piattini Mario
Published: February 2022

In order to improve and sustain their competitiveness over time, organisations nowadays need to undertake different initiatives to adopt frameworks, models and standards that will allow them to align and improve their business processes. In spite of these efforts, organisations may still encounter governance and management problems. This is whereRisk Management (RM)can play a major role, since its purpose is to contribute to the creation and preservation of value in the context of the organisation's processes. RM is a complex and subjective activity that requires experience and a high level of knowledge about risks, and it is for this reason that standardisation institutions and researchers have made great efforts to define initiatives to overcome these challenges. However, the RM field nevertheless presents a lack of uniformity in its terms and concepts, due to the different contexts and scopes of application, a situation that can generate ambiguities and misunderstandings. To address these issues, this paper aims to present an ontology calledSRMO (Software Risk Management Ontology), which seeks to unify the terms and concepts associated with RM and provide an integrated and holistic view of risk. In doing so, the Pipeline framework has been applied in order to assure and verify the quality of the proposed ontology, and it has been implemented in Protégé and validated by means of competency questions. Three application scenarios of this ontology demonstrating their usefulness in the software engineering field are presented in this paper. We believe that this ontology can be useful for organisations that are interested in: (i) establishing an RM strategy from an integrated approach, (ii) defining the elements that help to identify risks and the criteria that support decision-making in risk assessment, and (iii) helping the involved stakeholders during the process of risk management.
Efficient 3D Spatial Queries for Complex Objects
Authors: Teng Dejun , Liang Yanhui , Vo Hoang , Kong Jun , Wang Fusheng
Published: February 2022

3D spatial data has been generated at an extreme scale from many emerging applications, such as high definition maps for autonomous driving and 3D Human BioMolecular Atlas. In particular, 3D digital pathology provides a revolutionary approach to map human tissues in 3D, which is highly promising for advancing computer-aided diagnosis and understanding diseases through spatial queries and analysis. However, the exponential increase of data at 3D leads to significant I/O, communication, and computational challenges for 3D spatial queries. The complex structures of 3D objects such as bifurcated vessels make it difficult to effectively support 3D spatial queries with traditional methods. In this article, we present our work on building an efficient and scalable spatial query system,
iSPEED,
for large-scale 3D data with complex structures. iSPEED adopts effective progressive compression for each 3D object with successive levels of detail. Further, iSPEED exploits structural indexing for complex structured objects in distance-based queries. By querying with data represented in successive levels of details and structural indexes, iSPEED provides an option for users to balance between query efficiency and query accuracy. iSPEED builds in-memory indexes and decompresses data on-demand, which has a minimal memory footprint. iSPEED provides a 3D spatial query engine that can be invoked on-demand to run many instances in parallel implemented with, but not limited to, MapReduce. We evaluate iSPEED with three representative queries: 3D spatial joins, 3D nearest neighbor query, and 3D spatial proximity estimation. The extensive experiments demonstrate that iSPEED significantly improves the performance of existing spatial query systems.
An RDMA-enabled In-memory Computing Platform for R-tree on Clusters
Authors: Xiao Mengbai , Wang Hao , Geng Liang , Lee Rubao , Zhang Xiaodong
Published: February 2022

R-tree is a foundational data structure used in spatial databases and scientific databases. With the advancement of networks and computer architectures, in-memory data processing for R-tree in distributed systems has become a common platform. We have observed new performance challenges to process R-tree as the amount of multidimensional datasets become increasingly high. Specifically, an R-tree server can be heavily overloaded while the network and client CPU are lightly loaded, and vice versa.

In this article, we present the design and implementation of Catfish, an RDMA-enabled R-tree for low latency and high throughput by adaptively utilizing the available network bandwidth and computing resources to balance the workloads between clients and servers. We design and implement two basic mechanisms of using RDMA for a client-server R-tree data processing system. First, in the fast messaging design, we use RDMA writes to send R-tree requests to the server and let server threads process R-tree requests to achieve low query latency. Second, in the RDMA offloading design, we use RDMA reads to offload tree traversal from the server to the client, which rescues the server as it is overloaded. We further develop an adaptive scheme to effectively switch an R-tree search between fast messaging and RDMA offloading, maximizing the overall performance. Our experiments show that the adaptive solution of Catfish on InfiniBand significantly outperforms R-tree that uses only fast messaging or only RDMA offloading in both latency and throughput. Catfish can also deliver up to one order of magnitude performance over the traditional schemes using TCP/IP on 1 and 40 Gbps Ethernet. We make a strong case to use RDMA to effectively balance workloads in distributed systems for low latency and high throughput.
(Invited) Artificial Photosynthesis on III-Nitride Nanowire Arrays
Authors: Mi Zetian
Published: February 2022

High efficiency artificial photosynthesis, that can convert solar energy directly into chemical fuels, has been extensively investigated. To date, however, success in finding abundant visible-light active photocatalyst has been very limited. Recently, metal-nitrides (e.g. InGaN) have attracted considerable attention for applications in artificial photosynthesis, due to their excellent stability and tunable energy bandgap across nearly the entire solar spectrum. Moreover, InGaN is the only known material whose energy bandgap can straddle the redox potential of water under deep visible and near-infrared light irradiation. In this context, we have investigated the design, fabrication, and performance characterization of multi-band InGaN/GaN nanowire photocatalysts and photoelectrodes for solar water splitting and carbon dioxide reduction. In this work, InGaN nanowires are grown on Si substrate by molecular beam epitaxy. The water splitting reaction takes place on the nonpolar surface (m-planes) of InGaN nanowire photocatalysts. By optimizing the surface charge properties through controlled Mg dopant incorporation, the efficiency for solar-to-hydrogen conversion is enhanced by nearly two orders of magnitude. The absorbed photon conversion efficiency reaches ~90% with optimum Mg doping concentration. The significantly enhanced efficiency is directly related to the optimized surface electronic properties that lead to both efficient water oxidation and proton reduction. A solar-to-hydrogen conversion efficiency of 3.3% and stability >500 hours was demonstrated in photocatalytic overall water splitting. High stability of these nanowires is attributed to the N-rich surfaces of GaN nanowire structures, which protects against photocorrosion and oxidation. We have further demonstrated multi-band InGaN/GaN nanowire photoelectrodes monolithically integrated on a Si solar cell wafer. The tandem PEC device consists of a planar n+-p Si solar cell wafer and p-InGaN nanowire segments. The p-InGaN nanowire arrays are designed to absorb the ultraviolet and visible solar spectrum. The remaining photons with wavelengths up to 1.1 µm are absorbed by the underlying planar Si p-n junction. Such a monolithically integrated photocathode promises solar-to-hydrogen conversion efficiency >20%. With the use of such a photoelectrode, we have also demonstrated that syngas, a key feedstock to produce methanol and liquid fuels in industry, can be produced from a CO2 and H2O with a benchmark turnover number of 1330 and a desirable CO/H2 ratio of 1:2. Work is currently in progress to achieve high efficiency syngas and methanol generation in an aqueous photoelectrochemical cell.
Nano-Bio Assemblies Based on Natural and Artificial Proton Pump for Photocatalytic Hydrogen Production
Authors: Rozhkova Elena
Published: February 2022

Bioinspired photocatalytic transformation of solar energy and water to clean fuels such as hydrogen using semiconductors is among the most promising renewable energy technologies. “Greener” schemes of photocatalytic visible-light hydrogen production along with inorganic material utilize biological structures capable of light-harvesting, water splitting, or proton reduction. We have been developing visible-light-driven nano-bio photocatalysts for hydrogen production based on non-covalent assemblies of the natural and synthetic membrane proton pump and TiO2 semiconductor nanoparticles. A natural membrane complex of retinal-bearing proton pump bacteriorhodopsin (also known as purple membranes, PM) from the extremophile organism Halobacterium salinarum has been attracting an attention of researchers owing to its exceptional robustness, excellent photophysical properties, and structure−functional elegance. We demonstrated applicability of PMs in sunlight transformation systems constructed from TiO2, boosted with introduction of reduced graphene oxide rGO, or more recently, constructed as entirely synthetic PM – semiconductor architecture using cell-free synthetic biology approach. Fusing nanotechnology and synthetic biology approaches allows for systemic manipulation at the nanoparticle−bio interface toward directed evolution of energy nanomaterials and nanosystems.

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P. Wang, N.M. Dimitrijevic, A.Y. Chang, R.D. Schaller, Y. Liu, T. Rajh, E.A. Rozhkova, ACS Nano, 8(8), 7995-8002 (2014)

P. Wang, A. Y. Chang, V. Novosad, V. V. Chupin, R.D. Schaller, E. A. Rozhkova, ACS Nano, 11 (7), pp 6739–6745 (2017)

E.A. Rozhkova, P. Wang. In Nanomaterials for Photocatalytic Chemistry, 12, 195-227 (2016)
PARP-1 Inhibition Repressed Imbalance of Th17 and Treg Cells in Preterm Rats with Intrauterine Infection-Induced Acute Respiratory Distress Syndrome by Reducing the Expression Level of IL-6
Authors: Chen Qian , Zhang Ensheng , Wang Chun , Zhang Peipei , Huang Lei
Published: February 2022

Background. Abundant reports have uncovered an imbalance of Treg and Th17 cells in pulmonary diseases. Hereon, we intend to explore the impact of PARP-1 on the imbalance of Th17/Treg and the potential mechanism in premature rats with acute respiratory distress syndrome (ARDS). Methods. Preterm ARDS infants and healthy term infants were enrolled in this investigation. To induce a rat model of ARDS, E.coli suspension was given to rats through two vaginal dilator-guided intramuscular injections. H&E staining was used to perform histopathological examination. Flow cytometry was employed to assess the proportion of Th17 or Treg cells accounted for CD4+ T cells. ELISA was applied to measure levels of IL-6, IL-17A, and IL-10 in the serum of ARDS patients. Moreover, the mRNA and protein expression levels of PARP-1, IL-6, IL-17A, and IL-10 were detected through qRT-PCR and western blotting. Results. An increased Th17/Treg ratio was observed in preterm infants and rats with ARDS. The PARP-1 expression level was raised in the lung tissues of ARDS rats, and PARP-1 downregulation alleviated E.coli-induced lung injury in preterm rats. Expression levels of PARP-1, IL-6, and IL-17A were raised, and the IL-10 level was reduced in the lung tissues of rats after E.coli treatment, which was all reversed by PARP-1 suppression. Importantly, the ratio of Th17/Treg differentiated from purified CD4+ T cells of the E.coli + PARP-1 inhibitor group was elevated by recombinant IL-6. Conclusion. PARP-1 downregulation repressed the imbalance of Th17 and Treg cells via reducing the expression level of IL-6, implying that PARP-1 may be a promising target for ARDS therapy.
Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm
Authors: Tong Jin , Sun Jian
Published: February 2022

With the vigorous development of new media technologies such as Internet of Things, big data, and cloud computing, data-based sensor news (SN) will become the trend of news reporting in the future and the new normal of news production. Under this background, this paper further analyzes the relationship between SN production mode and traditional news production, including the inheritance of traditional news production value concept, as well as the breakthrough and change in form, media, and effect. In this paper, collaborative filtering (CF) algorithm is improved to solve the problems of data sparseness, user interest migration, and scalability in CF technology. In the calculation of news content similar degree (SD), the influence of part of speech and position of feature words in news is also considered, and the time window is used to establish a model that adapts to the change of user interest with time. In this method, the contribution degree of different attributes to distinguishing users is considered, and the attribute SD between users is accurately calculated, which effectively improves the accuracy of SN data acquisition results.
Research on Early Warning of Financial Crisis of Listed Companies Based on Random Forest and Time Series
Authors: Zhang Chi , Zhong Huaigong , Hu Aiping
Published: February 2022

The global economy has entered a new normal, and the economic environment is evolving at a rapid pace. This requires the establishment of a financial crisis early warning system that can be dynamically analyzed based on historical data information. To address this research objective, this study proposes a k-fold random forest algorithm combined with a time series analysis model as an early warning algorithm for corporate financial crises. The algorithm takes advantage of the ability of the time series analysis model to make short-term forecasts of historical data and uses the time series analysis model to make forecasts of newly constructed financial index data. The k-fold random forest is used to analyze the financial situation of the predicted financial data and achieve the purpose of dynamic financial crisis early warning. The experimental results show that the prediction accuracy of the financial crisis early warning model based on the random forest algorithm and time series is 89%, which indicates that the model is effective and feasible.
[Retracted] Construction and Application of Music Audio Database Based on Collaborative Filtering Algorithm
Authors: Zheng Jianfei
Published: February 2022

In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database.
Chinese Character Learning Information Platform Based on Multimedia Information Technology
Authors: Kang Bochun , Kang Sicheng
Published: February 2022

MIT (multimedia information technology) has brought profound changes to education, and MIT teaching has become an effective teaching method. Based on the teaching concept of MIT and curriculum integration, the connotation and goal of MIT Chinese and the Chinese character learning information platforms are clarified. In this paper, the design of a Chinese character learning information platform based on MIT is chosen as the study object. Starting with the investigation and analysis of the application of multimedia teaching resources by teachers in oral Chinese teaching, the problems in the construction of Chinese teaching resources are summarized. This paper combines the characteristics of domain ontology with those in the process of word segmentation to disambiguate ambiguous fields by calculating the strength of semantic association, so as to maximize the speed of ambiguity identification and resolution without losing AR (accuracy rate). After experiments, the system found a balance between RR (recall rate), AR, and the speed of Chinese character segmentation.
Genetic Transformability of Selected Kenyan Groundnut (Arachis hypogaea L.) Genotypes with IPT Gene Using Cotyledonary Node Explants
Authors: Limbua Purity G. , Ngugi Mathew P. , Oduor Richard O.
Published: February 2022

Groundnut (Arachis hypogaea L.) is an important crop in terms of income and nutrition. Despite its importance, groundnut yield is limited by environmental factors such as drought. This work reports the genetic transformability of Kenyan groundnut (Arachis hypogaea L.) genotypes with isopentenyl transferase (IPT) gene towards drought tolerance. The cotyledonary nodes of six Kenyan adapted groundnuts genotypes (ICGV 12991, CG 7, Red Valencia, ICGV 90704, Chalimbana, and JL 24) were transformed using Agrobacterium tumefaciens strain EHA 101 carrying PNOV-IPT binary vector containing an IPT gene, which was driven by SARK promoter and terminated by TNOS terminator. The vector also contained the phosphomannose isomerase (PMI) gene for the selection of transformed tissues. Putative transformants were tested for the presence of the transgene by PCR designed to amplify the IPT gene sequence. Gene expression was confirmed by RT-PCR. Transformation frequency was calculated as a percentage of the number of putative transformants divided by the total number of infected cotyledonary nodes. This ranged from 9.87% for ICGV 90704 to 19.77% for JL 24. Transformation efficiency was calculated as a percentage of the number of PCR positive plants divided by the total number of cotyledonary nodes infected. This ranged from 0% for ICGV 12991 and Chalimbana to 1.74% for JL 24. The data suggest the possibility of transforming groundnuts with the IPT gene and regenerating normal transgenic plants. This information will be useful during the transformation of groundnut towards different factors that affect production.
Discriminative Similarity-Balanced Online Hashing for Supervised Image Retrieval
Authors: Chen Chen , Wang Xiaoqin , Chen Xiao , Lan Rushi , Liu Zhenbing , Luo Xiaonan
Published: February 2022

When virtualizing large-scale images of the real world, online hashing provides an efficient scheme for fast retrieval and compact storage. It converts high-dimensional streaming data into compact binary hash codes while saving the structural characteristics between samples into the Hamming space. Existing works usually update the hashing function based on the similarity between input data, or design a codebook to assign code words for each single input sample. However, assigning code words to multiple samples while retaining the balanced similarity of the image instances is still challenging. To address this issue, we propose a novel discriminative similarity-balanced online hashing (DSBOH) framework in this work. In particular, we first obtain the Hadamard codebook that guides the generation of discriminative binary codes according to label information. Then, we maintain the correlation between the new data and the previously arrived data by the balanced similarity matrix, which is also generated by semantic information. Finally, we joined the Hadamard codebook and the balanced similarity matrix into a unified hashing function to simultaneously maintain discrimination and balanced similarity. The proposed method is optimized by an alternating optimization technique. Extensive experiments on the CIFAR-10, MNIST, and Places205 datasets demonstrate that our proposed DSBOH performs better than several state-of-the-art online hashing methods in terms of effectiveness and efficiency.
Effects of Sensory Garments on Sleep of Children with Autism Spectrum Disorder
Authors: Mische Lawson Lisa , Foster Lauren , Hodges Morgan , Murphy Mia , O’Neal Melissa , Peters Lesan
Published: February 2022

Objective. The purpose of this study is to assess the effectiveness of the use of sensory garments for improving sleep in children with autism spectrum disorder. Method. Using a single-subject ABAB reversal design, the researchers evaluated the effectiveness of a sensory garment on sleep duration, sleep latency, and parental stress related to a child’s sleep. Four children aged 4–10 participated. We measured sleep duration and sleep latency using the Garmin watches and parent-report sleep logs, parent stress using the Parenting Stress Index Short Form, and sleep behaviors using the Children’s Sleep Habits Questionnaire. Results/Discussion. Data showed variable effects on sleep duration and latency across children. The oldest child with the hyposensitive sensory patterns experienced the greatest sleep improvements. All parents experienced stress from daily life, and some reported increased stress due to study participation. Future research is recommended to further investigate the effectiveness of sensory garments on sleep for children with ASD. Therapists are encouraged to evaluate children’s development and sensory preferences prior to recommending sensory garments for sleep.
Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine
Authors: Ai Ke , Yuan Ding , Zheng Jie
Published: February 2022

Gastric cancer is still the fifth most common malignant tumor in the world and has the fourth highest mortality rate in the world. Gastric cancer is difficult to treat because of its unobvious onset, low resection rate, and rapid deterioration. Therefore, humans have been working hard to combat gastric cancer. At present, the most commonly used treatment method is radiotherapy. However, this method will damage the normal tissues of the irradiated area while treating malignant tumor cells. It not only has side effects of damage to the patient’s skin and mucous membranes but also needs high-rate radiotherapy and has high cost for chemotherapy. In order to solve these problems, it is necessary to find new treatment methods. This article proposes the use of Chinese medicine to invigorate the spleen to inhibit human gastric cancer cells. This article combines modern machine learning technology with traditional Chinese medicine and combines traditional Chinese medicine physiotherapy with Western medicine nude mouse transplantation experiments. The treatment of tumors in Chinese medicine is based on the theory of Chinese medicine and has different characteristics. Western medicine has the advantage of permanently injuring patients. The process of the experiment is to transplant human-derived gastric cancer cells into nude mice. After grouping treatments and obtaining comparative data, deep learning techniques are used to analyze the properties of Chinese medicines for strengthening the spleen and to compare the properties of Chinese medicines for strengthening the spleen. The experimental results showed that the tumor inhibition rate of mice using fluorouracil was 18%, the tumor inhibition rate of mice using low-dose Chinese medicine was 16%, and the tumor inhibition rate of mice using high-dose Chinese medicine reached 52%. 80 days after the experiment, the survival rate of mice using high-dose Chinese medicine is 100% higher than that of mice without treatment.
Impact of Energy Consumption, Economic Growth, and FDI through Environmental Kuznets Curve: Perspective from Belt and Road Initiative and Pakistan
Authors: Zubedi Asma , Jianqiu Zeng , Ali Qasim , Memon Imran , Zubedi Erum
Published: February 2022

A significant corridor under the “Belt and Road Initiative” is announced by the government of China for numerous transport and energy projects. China focuses greatly on the sustainability of these projects, in accordance with Green Investment Principles. For this, environmental, social, governance, and technological considerations are taken into account. This paper offers a novel assessment of the impact of green sustainability under BRI and the current emission scenario in Pakistan. The question that needs to be addressed here is whether the projects under Belt and Road Initiative are as environment friendly as they claim. In the first phase of this study, we review theories establishing evidence based on existing literature to determine the influence of Belt and Road on economic development. In the next phase, we use Environment Kuznets Curve and apply estimates from Johansson’s Cointegration Test to quantify the present environmental scenario of Pakistan and probable environmental risk portrayed by the China-Pakistan Economic Corridor (CPEC). This paper helps in determining the relationship between carbon emission and three major variables including energy consumption, economic growth, and foreign direct investment (FDI). Cointegration analysis is applied on time series data comprising of a 40 years’ period from 1979 to 2019. The paper finds that (i) the belt and road corridors could substantially improve trade, foreign investment, and living conditions for citizens in participating countries; (ii) moreover, this study confirms the presence of one cointegrated equation suggesting the presence of a long term relationship between the CO2 and independent variables and a short run association running from EnC to CO2. The study also proposes a Green BRI Model which formulates a body that devises environmental performance standards, reviews procedures, and ensures that they get implemented on all CPEC projects. The Belt and Road Initiative increases emissions among participating countries. China and corridor economies need to adopt strict policy reforms that increase transparency, expand trade, improve debt sustainability, and mitigate environmental, social, and corruption risks.
Probe Sonicated Synthesis of Bismuth Oxide (Bi2O3): Photocatalytic Application and Electrochemical Sensing of Ascorbic Acid and Lead
Authors: Kusuma K. B. , Manju M. , Ravikumar C. R. , Dileepkumar V. G. , Kumar A. Naveen , Santosh Mysore Sridhar , Murthy H. C. Ananda , Gurushantha K.
Published: February 2022

A simple and low‐cost and highly calibrated probe sonication method was used to prepare bismuth oxide nanoparticles (Bi2O3 NPs). The formation of a well‐crystalline sample at the end of the product has been further calcined at 600°C for 2 hrs. The powder X‐ray diffraction (PXRD) patterns of the NPs substantiated the monoclinic structure (space group P21/c), and the average crystallite size was found to be 60 nm, which was also confirmed by transmission electron microscopic (TEM) studies. Scanning electron microscopic (SEM) images depicted highly porous Bi2O3 NPs with little agglomeration. Utilizing diffused reflectance spectra (DRS) data, the energy bandgap (Eg) value of 3.3 eV was deduced for Bi2O3 NPs, and their semiconductor behavior has been confirmed. Two dyes, methylene blue (MB) and acid green (AG) were utilized for degradation studies using Bi2O3 NPs under UV light irradiation (from 0 to 120 min). The photocatalytic degradation was found to be maximum for MB (93.45%) and AR (97.80%) dyes. Cyclic voltammetric (CV) and sensor studies using the electrochemical impedance spectroscopy (EIS) were performed. The specific capacitance value of 25.5 Fg-1 was deduced from the cyclic voltammograms of the Bi2O3 electrode in 0.1 N HCl with a scan rate of 10 to 50 mV/s. From the obtained EIS data, the Bi2O3 electrode showed pseudocapacitive characteristics. The prepared electrodes also exhibited high sensitivity towards the detection of ascorbic acid and lead. Hence, sonochemically synthesized Bi2O3 NPs are possibly hopeful for excellent photocatalytic and electrochemical sensing of biomolecules.
Computed Tomography Image: Guided Needle Biopsy in the Diagnosis of Lung Malignant Tumors under Artificial Intelligence Algorithm
Authors: Chen Deping , Wang Meng , Li Weiping , Li Xin , Hou Haiwen , Shan Hui
Published: February 2022

The computed tomography (CT) image-guided needle biopsy was applied in the diagnosis of lung malignant tumors based on artificial intelligence (AI) algorithm under convolutional neural network (CNN) to explore the effect of artificial intelligence algorithms segmentation in needle biopsy surgery and to guide the diagnosis of lung malignant tumors. The subjects of the study were 100 patients with lung malignant tumors admitted to the hospital. The cases were diagnosed as lung cancers by CT, and they were divided into two groups with 50 people in each group. Among them, 50 people in the control group did not use algorithms for guidance and 50 people in the experimental group used algorithms for guidance. The 50 patients who received needle biopsy without guidance of any algorithm were included in the control group. The results showed that the average coincidence rate of automatic segmentation and manual segmentation by the artificial intelligence algorithm was 97.46%, and the average false positive rate (FPR) and false negative rate (FNR) were 0.07% and 0.08%, respectively. The segmentation time of the algorithm group was 12.5 s, which was significantly shorter than the 36.11 s of the control group, and the segmentation speed was significantly faster. The positive rate of pathology in the control group was 78%, and the pathological positive rate of the algorithm group was 80%. The difference in the pathological positive rate between the two groups was 2%, and the detection effect of the pathological positive rate in the algorithm group was close to that in the control group, which was of positive significance in pathological detection. The satisfaction rate of patients in the algorithm group with the detection effect was 88%, and that of the patients in the control group was 94%. The difference in the detection satisfaction rate between the two groups was 6%. The number of patients with pneumothorax in the algorithm group was 22, the number of patients with bleeding was 21, and the number of patients with infection was 2. The number of patients with pneumothorax in the control group was 17, the number of patients with bleeding was 19, and the number of patients with infection was 3. The patients had a higher probability of pneumothorax and bleeding and a lower probability of infection; there was no significant difference in the incidence of complications between the two groups of patients. In summary, the intelligent algorithm was effective and feasible in segmenting lesions, and the accuracy of segmentation could meet the clinical needs, which can be used as a reference for surgery. CT images based on artificial intelligence algorithms were a good way to guide the needle biopsy in the diagnosis of lung malignant tumors, improving the accuracy and sensitivity of the diagnosis of lung malignant tumors.
News and Public Opinion Multioutput IoT Intelligent Modeling and Popularity Big Data Analysis and Prediction
Authors: Yan Hao
Published: February 2022

Based on the news and public opinion multioutput Internet of Things architecture, this article analyzes and predicts its popularity with big data. Firstly, the model adopts a three-tier architecture, in which the bottom layer is the data layer. It is mainly responsible for the collection of the terminal sensor data of the Internet of Things, and it uses intelligent big data as the data warehouse. Secondly, the computing layer on the data layer mainly provides the computing framework. Using the open-source SQL query engine, a cluster environment based on memory computing is constructed to realize the parallelization of data computing. It is used for interactive operations between the system and users. It receives and forwards the query requests submitted by the client browser, transmits them to the server cluster for execution, and displays the results in the browser. The end is displayed to the user. After that, combined with the needs of the development and application of news and public opinion big data, the data collection process was analyzed and designed, and the distributed data collection architecture was built. The intelligent Internet of Things was adopted for data storage, the data storage structure was analyzed and designed, and the data storage structure was designed to avoid catching. The repeat check algorithm is used to repeatedlystore the obtained page data. At the same time, according to the analysis of the business needs of the news and public opinion information platform, the overall functional structure of the platform was designed. The database and platform interface were designed in detail. The simulation results show that the model realizes the statistical query of the collected sensor alarm information and historical data on the user system, combines the historical operating data to analyze the relationship between the supply/return water temperature of the heat exchange station and the outdoor temperature, and realizes chart visualization of data analysis.
Total Hip Arthroplasty with a Cementless Acetabular Component and Impaction Bone Grafting for Dysplastic Osteoarthritis Complicated by Multiple Myeloma
Authors: Ogawa Ryo , Hirao Maki , Umezu Taro , Yanagimoto Shigeru
Published: February 2022

Total hip arthroplasty (THA) for pathologic and impending fracture due to periacetabular multiple myeloma (MM) lesions has been reported. We report a case of radiographic progression of dysplastic osteoarthritis, complicated by periacetabular MM lesions, treated by THA. A 69-year-old female with a 13-year history of MM presented with right hip pain. Plain radiographs and CT showed that dysplastic osteoarthritis had progressed, while the periacetabular MM lesions remained unchanged. Pathologic fracture was not observed on MRI. THA with a cementless acetabular component and impaction bone grafting was done. Bone graft incorporation was confirmed on CT at 1 year after surgery. There were no signs of bone absorption or implant loosening at last follow-up 3 years after surgery. Due to the advances in the treatment of MM and antiresorptive drugs, cementless acetabular component and impaction bone grafting may be an option for dysplastic osteoarthritis complicated by acetabular bone loss due to MM.
Posture Monitoring of Basketball Training Based on Intelligent Wearable Device
Authors: Ma Zengyu , Hao Qi
Published: February 2022

The relevant content of China’s sport work clearly pointed out that it is necessary to improve the physical and mental health of the citizens. Combining the current development hotspots of mobile smart terminals and smart wearable devices, smart wearable devices are analyzed from the function and development history in order to find an effective combination of smart wearable devices and sports work. This article describes the youth as the main body receiving grassroots physical education of nonsports special students and basketball enthusiasts. In the process of physical education teaching, the problem of pain points is more prominent because most teenagers in the basketball education at the grass-roots level do not have the opportunity to get one-to-one private counseling. When there are problems in the operation, students cannot be guided to conduct training or practice by themselves. This paper aims to meet the demand of real-time monitoring of dribbling posture in basketball dribbling training and proposes a low-cost product solution to help teenagers carry out basketball dribbling training by themselves: intelligent wearable product of head wrist and dribbling assistant DribbleAid, which is used to monitor common bad posture problems of users in dribbling training and give corresponding reminders to deal with users’ pain points. In this work, various experiments have been carried out for the proposed method and system. A large number of experimental results show that the method designed in this paper can effectively monitor the basketball training posture.
The New Face of the Switch Flap in the Reconstruction of a Large Upper Eyelid Defect
Authors: Kuzmanović Elabjer Biljana , Bušić Mladen , Miletić Daliborka , Pleše Andrej , Bjeloš Mirjana
Published: February 2022

Reconstruction of a large defect after the removal of a massive malignant upper lid tumor is still a challenge in oculoplastic surgery. Our method of choice is Mustardé switch flap. Due to the lack of Mohs micrographic surgery and frozen section technique as well as waiting time of two weeks for histopathological results, we made modifications enabling us to reexcise in case of positive margins: the width of the pedicle of the flap was 7 mm allowing the length of the flap to be increased if needed, the lids were closed with temporary lateral tarsorrhaphy to protect the eye, and the lower lid is finally reconstructed in the second stage of the procedure. In three patients with malignant upper lid tumors, this method of reconstruction proved to be safe and effective with favorable long-term results.
Positive Association between the Triglyceride-Glucose Index and Hyperuricemia in Chinese Adults with Hypertension: An Insight from the China H-Type Hypertension Registry Study
Authors: Yu Chao , Wang Tao , Zhou Wei , Zhu Lingjuan , Huang Xiao , Bao Huihui , Cheng Xiaoshu
Published: February 2022

Background. Previous studies have revealed the triglyceride-glucose (TyG) index is closely related to hyperuricemia in the general population. However, this relationship in hypertensive patients has not been reported. The aims of this study are to investigate the relationship of the TyG index and hyperuricemia in adult Chinese hypertension. Methods. The China H-type Hypertension Registration (an observational, noninterventional, and real-world study) was conducted from March 01, 2018, to August 31, 2018, in 16 communities in Wuyuan of China. The TyG index was calculated from fasting triglycerides (mg/dL) and fasting glucose (mg/dL)/2. Hyperuricemia was categorized by uric acid concentration ≥420 μmol/L (7 mg/dL). Results. Overall average TyG index of 13,060 adults with hypertension was 8.87; age was 63.81 years. The TyG index was positively correlated with serum uric acid by multiple linear regression analyses (β = 38.03; 95% CI: 34.55 to 41.51). Coincidentally, logistic analyses also demonstrated the TyG index had a positive correlation with hyperuricemia (OR = 2.04; 95% CI: 1.87 to 2.24). Strong linear associations of the TyG index with serum uric acid and hyperuricemia were confirmed by restricted cubic spline analysis. Compared with subjects in the lowest quartile (7.13 to 8.44) of the TyG index, subjects in the 2nd–4th quartiles had 1.25 (95% CI: 1.10 to 1.41), 1.63 (95% CI: 1.43 to 1.86), and 2.79 (95% CI: 2.41 to 3.24) times greater incident risk of developing hyperuricemia. The TyG index had significant correlations between male patients and hyperuricemia (OR = 2.01) by subgroup analysis. Conclusion. Positive associations were found between the TyG index and serum uric acid and between the TyG index and hyperuricemia in adults with hypertension. This trial was registered at clinicaltrials.gov as ChiCTR1800017274.