bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Therapeutic Effects and Mechanisms of Gardenoside on Inflammatory
Injury via the INS/NF-κB Pathway in a T2DM Zebrafish Model
Authors: Liu Ping , Hou Mirong , He Jiawei , Zhou Jie , Wang Anna , Huo Chunjiang , Zhong Qiuhui , Zhang Jiaheng , Cheng Shaowu 
Published: February 2024

Objective:
This study utilized the zebrafish model to investigate the therapeutic effects of gardenoside on inflammatory damage in type 2 diabetes mellitus (T2DM) through the
INS/NF-κB pathway.

Methods:
The study employed 7-8-month-old AB strain zebrafish and induced T2DM models by
continuous exposure to a 2% glucose solution for 14 days. Control, model, gardenoside low-,
medium-, and high-dose groups (2.5 mg/L, 5 mg/L, 10 mg/L), and a metformin group were established. Parameters such as zebrafish body weight, fasting blood glucose levels, and behavioral
analyses were monitored. Fructosamine levels were measured using ELISA, while real-time polymerase chain reaction (Real-time PCR) was employed to assess the relative expression levels of
INS, IRS, NF-κB, IL-1β, and IL-6 genes. Intestinal histological examination was conducted to
observe inflammation levels.

Results:
Compared to the control group, the model group exhibited significantly increased blood
glucose levels, weight gain, and fructosamine content. High-speed locomotion time increased,
while low-speed locomotion time decreased. Relative expression levels of INS, NF-κB, IL-1β,
and IL-6 genes were elevated (P < 0.0001), whereas IRS relative expression levels decreased (P
E0.001). In comparison to the model group, the gardenoside and metformin groups demonstrated reduced blood glucose levels and no significant change in body weight. The gardenoside low
and medium dose groups, as well as the metformin group, showed reduced high-speed locomotion time and increased low-speed locomotion time. The relative expression levels of INS, NFκB, IL-1β, and IL-6 genes in the gardenoside and metformin groups decreased (P < 0.01 or P <
0.0001), while IRS relative expression levels increased (P < 0.001 or P < 0.0001).

Conclusion:
Gardenoside may suppress inflammatory responses, alleviate insulin resistance, and
ameliorate diabetes-related inflammatory symptoms, potentially through the regulation of the
INS/NF-κB signaling pathway.
Hybrid Electric Cycle with Two Stoke Engine Based Two Wheel Terrain
Vehicle
Authors: Kandasamy Prabaakaran , Esakkiappan Kaliappan , Govindan Ajai , Nanthivaraman Bharathraj , Rajendran Ranjith , Selvaraj Jaisiva 
Published: February 2024

Introduction::
Hybrid bicycles combine the features of road bikes and mountain bikes,
offering a versatile and practical mode of transportation. The focus of the hybrid bicycle project is
on energy conservation. Designed with a lightweight frame, comfortable seating, and efficient
wheels, these bikes provide riders with speed, control, and flexibility on various terrains. Their
adaptability, ease of use, and low maintenance requirements contribute to their growing popularity.

Method::
The hybrid bicycles in this project include a 250 W brushless motor, a lead-acid battery,
a controller, and a 110 cc internal combustion engine commonly used in motorcycles and scooters.
The engine operates on the principle of internal combustion, delivering optimal performance in a
compact and lightweight package. The BLDC motor, known for its efficiency and reliability, utilizes
permanent magnets to generate magnetic fields, eliminating the need for brushes and commutators.
Lead-acid batteries, widely used in various applications, store and release electrical energy
effectively. Controllers play a crucial role in managing electric motor operations, regulating
speed, torque, and direction of rotation.

Results::
Hybrid bicycles offer a sustainable and eco-friendly alternative to cars for commuting.
The paper explores hybrid bicycles' design, advantages, and potential impact on the environment
and public health, aiming to encourage their adoption for daily commutes and recreational activities.
The Speed range about 40-45 km/hr with 2.688 hrs of effective battery

Conclusion::
It also discusses the potential for advancements in battery-powered electric hybrid
bikes to enhance their appeal and impact. Ultimately, hybrid bicycles have the potential to reduce
traffic congestion and decrease air pollution.
Employment of Synchronized Chaotic SHA256 Random Number Generators along with their Application in the Field of Image Encryption
Authors: Muddineni Raveendra , Kundu Krishanu , Saravanakumar U. , Dheeraj Pedapalli Saam Prasanth 
Published: February 2024

Background:
Current work is associated with the deployment of synchronized chaotic
Secure Hash Algorithm (SHA256) random number generators along with their application in image
encryption.

Methods:
This study aimed to design a perfect communication system to encrypt and decrypt the
images via image processing. We introduced a new sliding mode scheme for master-slave fourdimensional
(4D) Lorenz-Stenflo systems to deal with the synchronization problem. Then, a new
synchronized dynamic secure hash algorithm (SHA-256) was implemented to generate the dynamic
random numbers.

Results:
An image encryption and decryption mechanism was implemented by using synchronized
chaotic master-slave systems corresponding to the generated hash values. Here, the master
systems were used for encryption, and the slave systems were responsible for the decryption
mechanism. The complete design of this paper was implemented in a Python environment.

Conclusion:
The demonstration of simulation results includes the state responses, strange attractors,
and synchronization error responses of master-slave chaotic systems with appropriate
switching functions and control input.

conclusion:
An image encryption and decryption mechanism has been implemented by using synchronized chaotic master-slave systems corresponding to the generated hash values

other:
The complete design of this paper has been implemented on python environment
Electric Vehicle Battery State of Charge and Charging Station Distance Estimation Using IoT
Authors: Raghavendran C.R. , Kaliappan E. , Kandasamy Prabaakaran 
Published: February 2024

Background:
The global transition to green energy and the rapid development of Electric
Vehicle (EV) technology, along with falling component costs, have fueled the growing popularity
of electric vehicles. To support the widespread adoption of EVs, an efficient and userfriendly
charging infrastructure is crucial.

Objective:
This work aims to propose a comprehensive EV charging system that addresses the rising
demand for charging stations, streamlines the charging process, and empowers EV drivers with essential
information. The primary focus is on an economical and effective booking system, enabling
users to locate nearby charging stations and make informed choices about their charging preferences

Methods:
We suggest developing a EVs Charging Finder App, serving as a central platform for
EV users to find nearby charging stations. The app will provide vital details, including ratings,
reviews, available time slots, charging duration estimates, and more. Users can also contribute
new charging station data, fostering app growth. Additionally, an alert system will notify users
when nearby charging slots become available, enhancing convenience for EV drivers.

Results:
The EVs Charging Finder App is anticipated to significantly enhance the accessibility
and convenience of EV charging. Users can effortlessly locate charging stations, assess quality
through reviews and ratings, and plan charging sessions based on real-time availability. The battery
voltage of 45.2 V is a critical parameter for monitoring the health and performance of the battery,
influencing the accuracy of state of charge (SoC) estimations and potentially impacting the
efficiency of the electric vehicle. The 47.7 km driven is a key factor in assessing energy consumption
and vehicle efficiency, which can affect the remaining state of charge in the battery. The battery's
state of charge (SOC) is at 85%, indicating a relatively high charge level. Knowing that the
charging station is available is crucial for planning charging activities, allowing users to proceed
without concerns about station availability. The booking time at 10:00 AM is essential for efficiently
managing charging infrastructure, especially in scenarios with high demand for charging
services. These data points collectively contribute to optimizing the charging experience and ensuring
the effective utilization of electric vehicle resources.

Conclusion:
The proposed EVs Charging Finder App offers a practical and efficient solution to
address the surging demand for charging stations. By providing comprehensive information and
real-time alerts, this system aims to make EV charging more accessible, user-friendly, and environmentally
sustainable.
Uncertainty Prediction of Offshore Wind Power based on Outlier Processing, Data Noise Reduction and the Deep Learning Method
Authors: Yu Zhen , Yang Yinguo , Lu Qiuyu , Wu Shuangxi , Zhu Yu 
Published: February 2024

Background:
Accurate prediction of offshore wind power is the basis for promoting
the safe and economic operation of offshore wind farms.

Objective:
This paper proposes an uncertainty prediction learning model based on outlier processing,
synchronous wavelet denoising and attention mechanism optimization to achieve accurate
prediction of offshore wind power.

Methods:
Firstly, the isolated forest is adopted to filter the outliers of offshore wind power data
and delete the error data caused by equipment or humans. Secondly, a syn-chrosqueezing wavelet
neural network (SWT) is applied to denoise historical wind power data, improve data quality, and
lay a foundation for accurate prediction. Next, the offshore wind power prediction method based
on IP-SO-LSTM-Attention is constructed to realize offshore wind power prediction, in which the
attention mechanism is applied to focus on the influence of important features on the output of
offshore wind power, and the improved particle swarm optimization algorithm is adopted to find
the best network structure of LSTM-Attention to optimize the prediction effect. After predicting
the point prediction results based on the SWT-IPSO-LSTM-Attention model, this paper sets
MAPE, RMSE, MAE and other indicators to evaluate the prediction effect.

Results:
The prediction error MAPE of the proposed model is 4.12%. It is 63.21% higher than
the benchmark model (SWT-BP) and 56.35% higher than the benchmark model (SWT-LSTM).

Conclusion:
Based on the point prediction results, this paper uses KDE (Gaussian) prediction results
to calculate the out-put curve under different confidence levels and provides decisionmaking
reference information for accurate decision-making.
Nanosensors from Logistics and Supply Chain Perspective: A Literature Review
Authors: Tunc Ilknur , Akyuz Goknur Arzu 
Published: February 2024

Introduction:
Nanosensors are one of the most recent technologies obtained from the
nanoworld. They have enhanced properties with potential applications in a wide spectrum of sectors.
They exhibit exceptional properties in temperature monitoring, hazardous gas and vapor detection,
humidity detection, as well as pathogens, toxins, and chemical contaminant detection. Among various
nanosensor applications, one of the most recent usages of nanosensor technology is in logistics
and supply chain management (SCM).

Aim:
This study aims at investigating the potential of nanosensor technology from logistics & SCM
perspective, and specifically questions how can this technology contribute to food supply chains.

Method:
A systematic comprehensive literature review was conducted with the keywords: nanosensors
& logistics, nanosensors & supply chain, nanosensors & smart packaging, and nanosensors &
food logistics for the date range 2001-2022, covering a broad set of references. Findings revealed
that nanosensors have significant potential in providing smart packaging, and improving traceability
and visibility across all the logistics and SCM processes including end-to-end transportation and
storage activities.

Result:
Hybrids of different types of nanosensors with the existing material identification systems
provide new levels of traceability and visibility in SCM, enabling accurate tracking of a variety of
parameters. The study has shown that the intersection of nanotechnology and logistics & SCM domains
is still immature.

Conclusion:
This intersection is especially critical for food supply chains for meeting their special
traceability requirements, and for serving food safety and organic food traceability across all stages
of logistics.
Scientific Writing – ChatGPT Versus Real-time Output: Addressing
Academician’s Concern
Authors: Anwar Firoz , Hosawi Salman Bakr I. , Al-Abbasi Fahad A. , Asar Turky Omar 
Published: February 2024

Background:
The advent of ChatGPT, an artificial intelligence (AI) model, has introduced
new challenges in educational practices, particularly in the realm of scientific writing at higher
educational institutions. The AI is trained on extensive datasets to generate scientific texts. Many
professors and academicians express concerns about the inclusion of AI chatbots in project execution,
interpretation, and writing within specialized subject curricula at the undergraduate and master’s
levels.

Methods:
To address these concerns, we employed the ChatGPT tool by posing a specific query
“Gynecomastia and the risk of non-specific lung disease, along with associated risk factors for workers
in the petrochemical industry”. We conducted a comparison between responses generated by
ChatGPT and real-time output from master’s students, examining document-to-document variation
on different dates.

Results and Discussion:
The AI chatbot failed to identify potential risk factors, in contrast to the
student response, which highlighted alteration in neutrophil levels, lung architecture, high IgE, elevated
CO2 levels, etc. The two responses did not align in terms of context understanding, language
nuances (words and phrases), and knowledge limitations (real-time access to information, creativity,
and originality of the query). A plagiarism check using the iThenticate software reported similarity
indices of 11% and 14%, respectively, in document-to-document analyses. The concerns raised by
academicians are not unfounded, and the apprehension regarding students utilizing ChatGPT in the
future revolves around ethical considerations, the potential for plagiarism, and the absence of laws
governing the use of AI in medical or scientific writing.

Conclusion:
While AI integration in the curriculum is feasible, it should be approached with a clear
acknowledgement of its limitations and benefits. Emphasizing the importance of critical thinking and
original work is crucial for students engaging with AI tools, addressing concerns related to ethics,
plagiarism, and potential copyright infringement in medical or scientific writings.
Effects of Lifestyle Interventions on Cardiovascular Disease Risk and Risk Factors Among Individuals at High Risk for Type 2 Diabetes: Protocol for a Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Demissie Getu Debalkie , Birungi Josephine , Haregu Tilahun , Thirunavukkarasu Sathish , Oldenburg Brian 
Published: February 2024

Background
Individuals at high risk for type 2 diabetes are also at an increased risk for developing cardiovascular disease (CVD). Although there are separate trials examining the effects of lifestyle interventions on absolute CVD risk among people at high risk for type 2 diabetes, a comprehensive evidence synthesis of these trials is lacking.

Objective
We will systematically synthesize the evidence on the effects of lifestyle interventions in reducing absolute CVD risk and CVD risk factors among people at high risk for type 2 diabetes.

Methods
We adhered to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) statement in reporting the details of this protocol. Randomized controlled trials of diabetes prevention that examined the effects of lifestyle interventions for at least 6 months on absolute CVD risk and CVD risk factors among individuals at high risk for type 2 diabetes will be eligible. We will systematically search the MEDLINE, Embase, PsycINFO, CENTRAL, and Scopus databases and ClinicalTrials.gov using a mix of Medical Subject Headings and text words. Two authors will independently screen the abstract and title of the articles retrieved from the search, followed by full-text reviews using the inclusion and exclusion criteria and data extraction from the eligible studies. Article screening and data extraction will be performed in the Covidence software. The primary outcome will be the changes in absolute 10-year CVD risk, as estimated by risk prediction models. The secondary outcomes are the changes in CVD risk factors, including behavioral, clinical, biochemical, and psychosocial risk factors, and incidence of type 2 diabetes.

Results
An initial database search was conducted in July 2023. After screening 1935 articles identified through the database search, 42 articles were considered eligible for inclusion. It is anticipated that the study findings will be submitted for publication in a peer-reviewed journal by the end of 2024.

Conclusions
This study will provide up-to-date, systematically synthesized evidence on the effects of lifestyle interventions on absolute CVD risk and CVD risk factors among individuals at high risk for type 2 diabetes.

Trial Registration
PROSPERO CRD42023429869; https://tinyurl.com/59ajy7rw

International Registered Report Identifier (IRRID)
DERR1-10.2196/53517
Improving Deep Learning Methods for Robust Methane Plume Detection using Alternative Input Representations
Authors: Satish Anagha , Bue Brian , Lee Jake , Thorpe Andrew , Cusworth Daniel , Ayasse Alana , Duren Riley 
Published: February 2024

Methane (CH4) is a prominent greenhouse gas responsible for about 20%
of all atmospheric radiative forcing. As we notice trends in increasing
global temperatures, understanding and detecting these emissions has
become increasingly important. This requires the creation of robust
greenhouse gas plume detectors. Previous work at the NASA Jet Propulsion
Laboratory has shown Convolutional Neural Networks (CNN) to be an
appropriate solution to map methane sources from future imaging
spectrometer missions, such as Carbon Mapper. However, current models
suffer from a high rate of false positives due to false enhancements in
the detected images.
We have compiled datasets from two Airborne Visible/Infrared Imaging
Spectrometer Next Generation (AVIRIS-NG) California campaigns. We then
trained a GoogleNet CNN Classifier model on each campaign. The baseline
current model uses a Unimodal column-wise matched filter (CMF). This
results in a model known to be sensitive to false enhancements, such as
water/water vapor, bright/dark surfaces, or confuser materials with
similar absorption wavelengths to methane. We first note improvements
between the Unimodal CMF model and a new Surface-Controlled CMF model,
whose dataset matches that of the Unimodal CMF model, but removes
enhancements not matching the absorption wavelength of methane. From
this, we note minimal improvement (1% increase in F1 score). We then
experiment with various auxiliary products measuring albedo (rgbmu,
SWALB), vegetation (NDVI, ENDVI), and water (h2o, NDWI) indices designed
to combat issues known to produce false enhancements. After training on
these new input representations for both campaigns, we noticed a
significant improvement in the multi-channel model’s results. We observe
an increase in the F1 score for classifying positive tiles from 0.78 to
0.86 when trained using auxiliary albedo indices, showing promise for
future use of auxiliary products in improving methane plume detectors.
MEMBANGUN LOYALITAS PADA KEDAI TEH: PERAN BRAND EXPERIENCE PADA BRAND LOVE
Authors: Octavia Yolanda , Tjokrosaputro Miharni 
Published: February 2024

Generasi Z yang terus mengikuti perkembangan tren saat ini telah menciptakan peluang bisnis baru, termasuk di dalamnya bisnis kedai teh. Tujuan dari penelitian ini adalah untuk menginvestigasi pengaruh pengalaman merek (Brand Experience - BE) dan kasih sayang terhadap merek (Brand Love - BL) terhadap loyalitas merek (Brand Loyalty - L) pada minuman teh siap saji yang sedang booming saat ini. Selain itu, penelitian ini juga akan mengeksplorasi peran brand love sebagai perantara antara pengalaman merek dan loyalitas merek terhadap minuman teh tersebut. Subjek penelitian ini adalah konsumen minuman teh yang berdomisili di Jakarta. Dari total 270 responden, 232 orang di antaranya memenuhi kriteria sebagai responden. Penelitian ini mengadopsi metode purposive sampling dengan menyebarkan kuesioner secara daring, dan data dianalisis menggunakan SmartPLS-SEM. Hasil penelitian ini menunjukkan bahwa brand experience memiliki dampak positif pada brand love dan brand loyalty. Selain itu, brand love juga memiliki dampak positif pada brand loyalty. Terlebih lagi, BE juga memiliki dampak positif pada brand loyalty melalui perantaraan brand love sebagai mediator. Penelitian ini diharapkan memberi kontribusi secara akademis dan industri tentang pentingnya BE dalam membangun loyalitas merek dengan BL sebagai variabel mediasi.

The millennial generation, characterized by their inclination to embrace current trends, has contributed to numerous new ventures, including the tea house industry. This research seeks to examine the impact of brand experience (BE) and brand love (BL) on brand loyalty (L) within the thriving ready-to-drink tea sector. Furthermore, this study explores the role of brand love as an intermediary between brand experience and brand loyalty for these beverages. The research's target population comprises tea consumers residing in Jakarta. Of a total of 270 respondents, 232 met the criteria as respondents. This study employed purposive sampling through online questionnaires to collect data, subsequently analyzing the data using SmartPLS-SEM. The findings of this investigation indicate that brand experience positively influences brand love and loyalty. Additionally, brand love positively impacts brand loyalty, while brand experience also positively affects brand loyalty, mediated by brand love. This research is expected to make an academic and industrial contribution regarding the importance of BE in building brand loyalty with BL as a mediating variable.
DAMPAK PANDEMI COVID-19 TERHADAP PEMILIHAN MODA TRANSPORTASI PADA SAAT SEBELUM PPKM, SAAT PPKM, DAN SETELAH PPKM
Authors: Edward Thomas 
Published: February 2024

One of the most frequently used means of moving is by using public transportation. Public transportation develops following the economic development of a region, the faster the region develops, more public transportation will be needed. However, since the pandemic of COVID-19 started there are many new policies have been issued by the government, both for the public and the private transportation. One of the policies that have been applied is new normal, where people who will carry out activities outside home must follow the health protocol as applicable to government regulation referred to as Large Scale Social Restrictions (LSSR).
Keywords: Public Transportation, New Normal, Covid-19, LSSR.
Salah satu sarana untuk berpindah tempat yang paling sering digunakan adalah transportasi umum. Transportasi umum berkembang mengikuti perkembangan ekonomi suatu wilayah, semakin pesat wilayah itu berkembang maka semakin banyak juga transportasi umum yang akan dibutuhkan. Namun sejak terjadinya pandemi COVID-19 banyak berlaku aturan-aturan baru yang dikeluarkan oleh pemerintah, baik pada angkutan umum ataupun angkutan pribadi. Salah satu peraturan yang diterapkan adalah New Normal, dimana masyarakat akan melakukan yang akan melakukan aktivitas diluar rumah harus mengikuti protokol Kesehatan sebagaimana berlaku pada aturan pemerintah yang disebut sebagai Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM).
Kata kunci: Transportasi Umum, New Normal, Covid-19, PPKM.