bluuebunny/crossref_metadata_2025 huggingface dataset:
Fully automated landmarking and facial segmentation on 3D photographs
Authors: Berends Bo , Bielevelt Freek , Schreurs Ruud , Vinayahalingam Shankeeth , Maal Thomas , de Jong Guido
Published: March 2024

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.
A qualitative study of Chinese parental perspectives on the causes of Tourette syndrome in children
Authors: Hu Yong , Yu Dan , Liu Zheng , Zhao Li , Zhang Lingli , Yang Chunsong
Published: March 2024

Parents’ beliefs and attitudes toward their children with Tourette syndrome (TS) influence treatment-seeking behaviors. This study aimed to explore and describe the Chinese parents’ perspectives on the causes of TS for their children. A qualitative study using semi-structured interviews was conducted with the parents of TS patients from a children’s hospital in western China from June to July 2021, and thematic analysis was performed to transcribe interviews and identify themes. A total of 13 participants were interviewed in this study. Five themes were developed in relation to the cognition of the causes of TS in parents, including physical problems, parenting and education problems, mental problems, bad habits, and neurological problems. Due to the insufficient awareness of TS, most parents repeatedly seek medical advice that they regarded the symptoms as physical problems or neurological problems. They generally felt guilty and blamed themselves for their parenting styles and education methods. And some parents attributed it to the poor psychological quality or the bad habits of children. Study findings showed a lack of scientific understanding of the causes of TS among parents further hindered the timely effective treatment for patients and affected the family relationships, which highlights the importance of public education and raising awareness of the disease.
Neurofilament light chain and glial fibrillary acid protein levels are elevated in post-mild COVID-19 or asymptomatic SARS-CoV-2 cases
Authors: Plantone Domenico , Stufano Angela , Righi Delia , Locci Sara , Iavicoli Ivo , Lovreglio Piero , De Stefano Nicola
Published: March 2024

Given the huge impact of the COVID-19 pandemic, it appears of paramount importance to assess the cognitive effects on the population returning to work after COVID-19 resolution. Serum levels of neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) represent promising biomarkers of neuro-axonal damage and astrocytic activation. In this cohort study, we explored the association between sNfL and sGFAP concentrations and cognitive performance in a group of 147 adult workers with a previous asymptomatic SARS-CoV-2 infection or mild COVID-19, one week and, in 49 of them, ten months after SARS-Cov2 negativization and compared them to a group of 82 age and BMI-matched healthy controls (HCs). sNfL and sGFAP concentrations were assessed using SimoaTM assay Neurology 2-Plex B Kit. COVID-19 patients were interviewed one-on-one by trained physicians and had to complete a list of questionnaires, including the Cognitive Failure Questionnaire (CFQ). At the first assessment (T0), sNfL and sGFAP levels were significantly higher in COVID-19 patients than in HCs (p < 0.001 for both). The eleven COVID-19 patients with cognitive impairment had significantly higher levels of sNfL and sGFAP than the others (p = 0.005 for both). At the subsequent follow-up (T1), sNfL and sGFAP levels showed a significant decrease (median sNfL 18.3 pg/mL; median sGFAP 77.2 pg/mL), although they were still higher than HCs (median sNfL 7.2 pg/mL, median sGFAP 63.5 pg/mL). Our results suggest an ongoing damage involving neurons and astrocytes after SARS-Cov2 negativization, which reduce after ten months even if still evident compared to HCs.
The impact of mindfulness apps on psychological processes of change: a systematic review
Authors: Macrynikola Natalia , Mir Zareen , Gopal Tishmattie , Rodriguez Erica , Li Sunnie , Cox Milann , Yeh Gloria , Torous John
Published: March 2024

Mindfulness-based interventions (MBIs) have demonstrated therapeutic efficacy for various psychological conditions, and smartphone apps that facilitate mindfulness practice can enhance the reach and impact of MBIs. The goal of this review was to summarize the published evidence on the impact of mindfulness apps on the psychological processes known to mediate transdiagnostic symptom reduction after mindfulness practice. A literature search from January 1, 1993, to August 7, 2023 was conducted on three databases, and 28 randomized controlled trials involving 5963 adults were included. Across these 28 studies, 67 outcome comparisons were made between a mindfulness app group and a control group. Between-group effects tended to favor the mindfulness app group over the control group in three psychological process domains: repetitive negative thinking, attention regulation, and decentering/defusion. Findings were mixed in other domains (i.e., awareness, nonreactivity, non-judgment, positive affect, and acceptance). The range of populations examined, methodological concerns across studies, and problems with sustained app engagement likely contributed to mixed findings. However, effect sizes tended to be moderate to large when effects were found, and gains tended to persist at follow-up assessments two to six months later. More research is needed to better understand the impact of these apps on psychological processes of change. Clinicians interested in integrating apps into care should consider app-related factors beyond evidence of a clinical foundation and use app databases to identify suitable apps for their patients, as highlighted at the end of this review.
Niche Tet maintains germline stem cells independently of dioxygenase activity
Authors: Tu Renjun , Ping Zhaohua , Liu Jian , Tsoi Man Lung , Song Xiaoqing , Liu Wei , Xie Ting
Published: March 2024

Ten-eleven translocation (TET) proteins are dioxygenases that convert 5-methylcytosine (5mC) into 5-hydroxylmethylcytosine (5hmC) in DNA and RNA. However, their involvement in adult stem cell regulation remains unclear. Here, we identify a novel enzymatic activity-independent function of Tet in the Drosophila germline stem cell (GSC) niche. Tet activates the expression of Dpp, the fly homologue of BMP, in the ovary stem cell niche, thereby controlling GSC self-renewal. Depletion of Tet disrupts Dpp production, leading to premature GSC loss. Strikingly, both wild-type and enzyme-dead mutant Tet proteins rescue defective BMP signaling and GSC loss when expressed in the niche. Mechanistically, Tet interacts directly with Bap55 and Stat92E, facilitating recruitment of the Polybromo Brahma associated protein (PBAP) complex to the dpp enhancer and activating Dpp expression. Furthermore, human TET3 can effectively substitute for Drosophila Tet in the niche to support BMP signaling and GSC self-renewal. Our findings highlight a conserved novel catalytic activity-independent role of Tet as a scaffold protein in supporting niche signaling for adult stem cell self-renewal.
Low thermal conductivity in a new mixed metal telluride Mn1.8(1)In0.8(1)Si2Te6
Authors: Shahid Omair , Yadav Sweta , Manna Kaustuv , Thakur Gohil S. , Prakash Jai
Published: March 2024

The first quaternary telluride, Mn1.8(1)In0.8(1)Si2Te6, of the Mn–In–Si–Te system has been synthesized and characterized. The phase shows ultralow thermal conductivity (0.28 W m−1 K−1 at 773 K) values.
Structure-based virtual screening of unbiased and RNA-focused libraries to identify new ligands for the HCV IRES model system
Authors: Kallert Elisabeth , Almena Rodriguez Laura , Husmann Jan-Åke , Blatt Kathrin , Kersten Christian
Published: March 2024

Using structure-based virtual screening, FRET and MST assays, novel ligands of the hepatitis C virus internal ribosome entry site were identified. This proof-of-concept study demonstrated the feasibility of RNA–ligand docking for hit identification.
Ba3SnGa10−xInxO20 (0 ≤ x ≤ 2): site-selective doping, band structure engineering and photocatalytic overall water splitting
Authors: Li Weihua , Lu Guangxiang , Ding Yanhong , Cong Rihong , Yang Tao
Published: March 2024

Site selective doping of In3+ into octahedral sites was studied by Rietveld refinements, and such doping reduced the band gap energy by lowering the CBM potential and eventually led to an enhancement in photocatalytic overall water splitting.
Biosynthesis of a new skyllamycin in Streptomyces nodosus: a cytochrome P450 forms an epoxide in the cinnamoyl chain
Authors: Song Yuhao , Amaya Jose A. , Murarka Vidhi C. , Mendez Hugo , Hogan Mark , Muldoon Jimmy , Evans Paul , Ortin Yannick , Kelly Steven L. , Lamb David C. , Poulos Thomas L. , Caffrey Patrick
Published: March 2024

Gene disruption identifies a class of cytochrome P450s that form epoxides in acyl chains of substituted cinnamoyl-containing natural products.
Synthesis of chiral sulfones via nickel-catalyzed asymmetric hydrogenation
Authors: Xiao Guiying , Wu Xiaoxue , Zhou Zhangtao , Hou Guohua
Published: March 2024

A Ni-catalyzed highly enantioselective hydrogenation of α,β-unsaturated sulfones was successfully realized achieving high yields (91–98%) and excellent enantioselectivities (up to 99.9% ee).
TASTE
Authors: Leonardi P. , Nascimbeni V. , Granata V. , Malavolta L. , Borsato L. , Biazzo K. , Lanza A. F. , Desidera S. , Piotto G. , Nardiello D. , Damasso M. , Cunial A. , Bedin L. R.
Published: March 2024

The discovery of the first transiting hot Jupiters (HJs), giant planets on orbital periods shorter than P ~ 10 days, was announced more than 20 years ago. As both ground- and space-based follow-up observations are piling up, we are approaching the temporal baseline required to detect secular variations in their orbital parameters. In particular, several recent studies have focused on constraining the efficiency of the tidal decay mechanism to better understand the evolutionary timescales of HJ migration and engulfment. This can be achieved by measuring a monotonic decrease in orbital period dP/dt < 0 due to mechanical energy being dissipated by tidal friction. WASP-12b was the first HJ for which a tidal decay scenario appeared convincing, even though alternative explanations have been hypothesized. Here we present a new analysis based on 28 unpublished high-precision transit light curves gathered over a 12-yr baseline and combined with all the available archival data, and an updated set of stellar parameters from HARPS-N high-resolution spectra, which are consistent with a main-sequence scenario, close to the hydrogen exhaustion in the core. Our values of dP/dt = −30.72 ± 2.67 and Q′* = (2.13 ± 0.18) × 105 are statistically consistent with previous studies, and indicate that WASP-12 is undergoing fast tidal dissipation. We additionally report the presence of excess scatter in the timing data and discuss its possible origin.
First spiral arm detection using dynamical mass measurements of the Milky Way disk
Authors: Widmark Axel , Naik Aneesh P.
Published: March 2024

We applied the vertical Jeans equation to the Milky Way disk in order to study non-axisymmetric variations in the thin disk surface density. We divided the disk plane into area cells with a 100 pc grid spacing and used four separate subsets of the Gaia DR3 stars, defined by cuts in absolute magnitude, that reach distances up to 3 kpc. The vertical Jeans equation is informed by the stellar number density field and the vertical velocity field; for the former, we used maps produced via Gaussian process regression; for the latter, we used Bayesian neural network radial velocity predictions, which allowed us to utilise the full power of the Gaia DR3 proper motion sample. For the first time, we find evidence of a spiral arm in the form of an over-density in the dynamically measured disk surface density, detected in all four data samples, which agrees very well with the spiral arm as traced by stellar age and chemistry. We fitted a simple spiral arm model to this feature and infer a relative over-density of roughly 20% and a width of roughly 400 pc. We also infer a thin disk surface density scale length of 3.3–4.2 kpc when restricting the analysis to stars within a distance of 2 kpc.
Experimental investigation on strength characteristics of autoclave aerated concrete block using natural pozzolanas
Authors: Kothapally Thangamani , Patil Ushadevi , Mounika Chigullapally , Naidu Gandi Mounika , B Geetha Kumari , Kotov Evgeny Vladimirovich
Published: March 2024

In this present study we have studied about the strength enhancement of autoclaved aerated block by using natural pozzolanas. Fly ash has been partially replaced with GGBS. GGBS has been used in place of fly ash at percentages of 6%, 12%, and 18% of the total amount of fly ash plus super plasticizer. GGBS improves resistance to damage from alkali silica reaction. It also reduces the thermal cracking. The role of super plasticizer is to reduce the water content in block.we have used 1% of super plastizer to the 1 litre of water. Each block's compressive strength is measured and computed for varying fly ash and GGBS ratios. It has been observed that replacing GGBS with 12% yields higher compressive strength than replacing it with 6% or 18%.
The Properties of Geo-polymer Concrete by Partial Replacement of Cement with GGBS & Fly ash
Authors: Rathna Chary Mudigonda , Hima Bindu Kotha , Shrisaila Haranatti Jagadish , Samineni Mohnika , Pavani Pasupuleti , Igorevich Rozhdestvenskiy Oleg
Published: March 2024

The production of Portland cement contributes to significant environmental pollution, with around 7% of global carbon dioxide emissions attributed to this industry. To address this issue, the construction sector seeks eco-friendly alternatives to conventional concrete. This project focuses on geopolymer concrete, which utilizes Fly ash and ground granulated blast-furnace slag (GGBS) can be utilized as a partial substitute for cement. The study investigates the effects of incorporating different percentages of fly ash (5%, 10%, 15%, 20%, and 25% as cement replacement) and GGBS (10%, 15%, 20%, 25%, and 30% as sand replacement) in geopolymer concrete. Compressive strength tests are conducted on cube specimens (150mm x 150mm x 150mm) at various ages (7 days, 14 days, and 28 days).
Efficiency of cement and sand in stabilizing the black cotton soil
Authors: Shrisaila Haranatti Jagadish , Vijayakumar Miryala , Rathna Chary Mudigonda , Hima Bindu Kotha , Sucharita Singha Soumya , Swapna V. , Viktorovna Nemova Darya
Published: March 2024

As the population rises and access to land decreases, an increasing number of buildings and other civil engineering structures must be constructed on weak or soft soil. Because of the soil's poor shear strength and substantial swelling and shrinking, a variety of ground improvement techniques, including soil stabilisation and reinforcing, are implemented to improve the mechanical behavior of the soil, hence boosting construction reliability. One of the most important soils is black cotton soil, which we intend to develop by utilising India's resources of sand and cement. In this research, several tests were conducted to determine the basic properties of soil. Instead of opting for the removal and replacement of unstable soil, soil modification is the only viable option because it saves both time and money. When exposed to changes in moisture content, they exhibit significant swelling and shrinking, making them the most challenging from an engineering standpoint. In this study, 2% cement was added at a steady rate while the sand percentage ranged from 10% to 40% which is utilised for the experiment was clean sand that had been passed through a 425 micron sieve and basic tests were performed to determine soil parameters. The physical properties of Black Cotton Soil determined & where the soil is of clay type & specific gravity is 2.6 and it is classified as CH. After determining the basic properties of virgin soil, the proctor test and CBR tests were performed by stabilizing with cement and sand. Sand is utilized in various percentages, such as 10%, 20%, 30%, and 40% of the total soil weight. MDD increased from 1.820 to 1.902 gm/cc. The CBR increased to 5.21% from 0.55% for 2.5mm penetration and 4.12% from 0.48% for 5mm penetration. As the percentage of sand increases, both thickness and cost drop. The CBR design chart yields a pavement thickness of 480mm for 20% sand and 2% cement, which is comparable to the minimum thickness of 475mm.
Revolutionizing Agricultural Harvesting with IoT Application
Authors: Sri Kakarla Udaya , Venkataramana A. , Gopinath S. , Shivaram K. , Yugendhar K. , Harika K.V.S.L. , Dixit Saurav
Published: March 2024

Traditional agriculture labor is characterized by its labor-intensive nature, relying heavily on manual labor. This approach is time-consuming and often results in low yields. Manual labor is susceptible to errors, leading to compromised crop quality. Moreover, the traditional method incurs high costs due to the need to hire numerous workers. Overall, traditional agriculture labor is inefficient, costly, and prone to quality issues, highlighting the need for more efficient and modernized agricultural practices. The current harvesting process for leafy greens relies heavily on manual cutting and individual knotting of each bunch, presenting significant challenges in terms of efficiency, labor shortage, and consistency in quality control. This labor-intensive methodology not only consumes valuable time but also strains increased operational inefficiencies. In this study, we address these challenges by proposing an innovative and automated harvesting system tailored for leafy greens. By integrating cutting-edge robotics and advanced sensing technologies, our solution aims to streamline the harvesting process, mitigating labor shortages, and reducing workforce strain. The system's intelligent automation ensures uniformity in bunching, enhancing both efficiency and the quality of the harvested produce. Through this research, we anticipate a transformative shift in the leafy greens harvesting industry, fostering increased productivity, labor optimization, and improved overall operational performance.
Optimization of Engineering Properties in Al-7175/SiC/B4C Alloy
Authors: Dheeraj Kumar Gajula , Sreenivasa reddy Nallimelli , Subbaratnam Bhavanasi , Seshappa Angadi , Krishna Bhargavi Y. , Iurevna Kseniia , Usanova
Published: March 2024

The growth of the industrial sector has resulted in a higher use of Aluminium Metal Matrix Composites (AMMCs). These composites are well-known for their exceptional mechanical and tribological qualities, attracting worldwide attention, especially in the automotive, architectural, and aerospace industries. Advanced Metal Matrix Composites (AMMCs) exhibit increased specific strength, improved strength-to-weight ratio at increasing temperatures, and better wear resistance when compared to the basic matrix without any additional components. SiC and B4C particle reinforcements are used to enhance the metallurgical and mechanical characteristics of the base matrix. Authors have used several production methods such as Powder Metallurgy for solid-state procedures and Stir Casting, Compo-casting, Squeeze Casting, and in-situ casting for liquid-state processes to create AMMCs. Stir casting is the most cost-effective and easy process for producing AMMC. This article provides an in-depth explanation of the procedure for producing Aluminium Matrix Metal Composites (AMMCs) utilising AL-7175 as the matrix material, together with B4C and SiC as reinforcements, with the stir casting technique.
Impact of TiB2 Particles on Improving rocessing Parameters of TiB2/Al Metal Matrix Composites
Authors: Seshappa Angadi , Prudhvi Raj Konka , Subbaratnam Bhavanasi , Chandrasekhar Rao Bandaru , Geetha Kumari B. , Divya Boya , Lakhanpa Sorabh
Published: March 2024

TiB2/Al MMCs, which are composite materials consisting of TiB2 particles embedded in an aluminium matrix, exhibit unique properties that make them very favourable for manufacturing highperformance aircraft blades. Nevertheless, the machinability of TiB2/Al MMCs continues to be a hurdle owing to the impact of TiB2 particles, hence restricting the extensive use of these materials. This research effectively examined the influence of TiB2 particles on processing parameters for TiB2/Al MMCs in order to meet manufacturing requirements. In addition, the research sought to determine the most favourable processing settings for these metal matrix composites, taking into account many considerations. The end-process variables, namely surface roughness and material removal rate (MRR), were assessed after the determination of the most favourable cutting velocity, feed rate, and depth of cut. The findings demonstrate substantial deviations from the strengthening of Metal Matrix Composites (MMCs) using titanium diboride (TiB2) nanoparticles. These results provide vital insights for enhancing the machining process of this material.
The effect of tooling design and properties of materials on fracture and deformation through equal channel angular pressing technique
Authors: Singh Nagendra , Kumar Agrawal Manoj
Published: March 2024

The submicrometer range of grain sizes was reached for AA5083 by using equal channel angular pressing at room temperature. While the submicrometer grains of AA5083 were stable up to annealing temperatures of 300 °C, the stability of these grains was only moderately maintained up to annealing temperatures of about 200 °C. Tensile tests conducted after one pass of equal channel angular pressing—that is, strain introduction of roughly one—showed a significant increase in the 0.2% proof stress and ultimate tensile stress values for each alloy. Concurrent with this improvement, the elongations to failure decreased. The analysis shows that the square root of the magnesium content in each alloy corresponds with the magnitudes of these stresses. In samples that were cold rolled, comparable values of proof stresses and ultimate tensile stress were obtained at equivalent strains. However, because of the induction of a very small grain size, elongations to failure were higher after applying equal channel angular pressing to similar strains greater than one. The effects of material constitutive behaviour, tool design, and friction conditions on metal flow, stress fields, and the tendency for tensile fracture during the equal channel angular pressing process were studied using a finite element modelling technique. A degree of non-uniform flow was noted that extended past the head and tail of the extrusion when materials were subjected to equal channel angular pressing with varying constitutive behaviours or when utilising tooling with a radiused front leg. It is anticipated that tool design and material qualities will have a considerable effect on tensile stresses and, in turn, the development of tensile damage during equal channel angular pressing.
Green synthesis of titanium dioxide nanomaterials from drypetes sepiaria leaves extract and their application environment pollutant remediation
Authors: Narasaiah Boya Palajonnala , Rajesh Dasari , Mabusab Pinjari , Shareef Shaik Noormohammad , Gajula Ramesh Kumar , Deepa R N Ashlin , Mittal Aman
Published: March 2024

The current report, an eco-friendly to environment benign to fabrication of Titanium Di-Oxide Nanomaterials (TDO-NMs) using Drypetes sepiaria Leaves aqueous extract, during the procedure, rather than using noxious and persistent chemicals throughout the experiment. Characterization of Titanium Di-Oxide Nanomaterials was revealed using UV–Vis spectroscopy, X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The stability analysis of Titanium Di-Oxide Nanomaterials was determined through dynamic light scattering (DLS), which indicated that the Titanium Di-Oxide Nanomaterials were stability around -39.4 mV and with an average size of 16.78 nm. The synthesized Titanium Di-Oxide Nanomaterials were carried out for the degradation of industrial textile pollutants dyes, such as Methylene Blue. The synthesized Titanium Di-Oxide Nanomaterials acts as a remarkable catalytic activity for the removal of environment pollutants dye degradation and promising material for the dye degradation.
Synthesis and characterization of gold nano particles using barleria cristata for antimicrobial and anticancer applications
Authors: Baskar Sivaraman , Kanithan Sivaraman , Srinivas Namani , Govinda Rao S. , Singla Tara
Published: March 2024

A nano revolution is necessary for the integration of nanoscience and medicine. Drug delivery, electronics, biological sciences, optics, and catalysis are among the fields in which metal nanoparticles find application. The objective of this work is to extract leaves from Barleria cristata in order to produce ecologically friendly gold nanoparticles (Au NP) for pharmacological studies. Several human pathogens were used to assess the biological mechanism and antibacterial properties of gold nanoparticles (AuNPs) in relation to their potential as a cancer treatment. An aqueous extract with a natural pH of 7.4 was obtained by reacting with 1 mM chloroauric acid (HAuCl4.3H2O), and it was then kept at room temperature. The colour changed from pale yellow to pink right away, indicating the reduction of Au3+ ions to Au 0. Using a UV-Visible spectrophotometer, the synthesised AuNPs were seen. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), field emission transmission electron microscopy (FE-TEM), and particle size distribution and zeta potential analysis are a few examples of analytical techniques. The disc diffusion method was used to confirm the extract's antibacterial properties in vitro on a few human illnesses. The MTT test was used to confirm the extract's anticancer properties on Hela carcinoma cell lines, revealing IC50 values of 50 μg/mL.
Waste cooking oil into sustainable and lucrative bio-diesels and their blend with diesel
Authors: Pinjari Mabusab , Lakkoju Babi , Vemulapalli Vandana , Tavanya Gunda , Mittal Vaibhav
Published: March 2024

The necessity to find an alternate fuel has arisen due to the rapid spike in cost of crude oil. In terms of economics, the alternative fuel should be more appealing than fossil fuels. Vegetable oils are used to make costly yet environmentally beneficial biodiesels. Utilizing leftover cooking oil to produce biodiesel is less expensive than using virgin oil. The bio-diesel have prepared from Waste cooking oil (WCO), collected from GITAM University hostel mess. This reaction has accelerated by the base catalysed the transesterification reaction with using ethanol. 1HNMR and IR spectrum analyses were used to describe the biodiesel that was produced. Numerous physico-chemical characteristics of biodiesels and their mixes with petroleum diesel were examined. The results of the investigation indicate that the blends from B10 to B40 have high flash points, lower pour points, and kinematic viscosity in the range of 2 to 5 cSt at 40 oC.
Customer Service 2.0: The Influence of Chatbots and AI Solutions"
Authors: Bhattaru Sarathsimha , Goli Mahendar , Swetha T. , Soujanya R. , Jain Alok
Published: March 2024

In recent years, chatbots have gained popularity as a result of breakthroughs in artificial intelligence and other technologies that lie behind the surface. In a similar vein, the growing interactivity of the internet and the enhanced capabilities of mobile devices have notably drawn a greater number of customers who are looking for improved and personalized customer care. This is the goal. Therefore, the purpose of this chapter was to gain a better understanding of the usage of chatbots by online businesses in order to throw light on the influence that it has on the happiness of customers with customer service. In this particular research endeavor, the conceptual framework was built using the commitment theoretical trust underpinning. It investigated trust, commitment, service quality, and technology in regard to the utilization of chatbots. As a consequence, the higher level of customer involvement that was achieved has resulted in increased information sharing and referrals to additional clients. The purpose of this chapter was to propose an integrated framework for anticipating the use of chatbots to increase customer bonding with businesses. The most important contribution was the compilation of a list of prerequisites that must be met in order to enhance customer involvement in the process of implementing chatbots.
An Analytical study on Navigating Sustainability Challenges and Opportunities in the era of AI and the Gig Economy
Authors: Nimmagadda Babu , Vangaveti Yugandhar , Aaluri Suresh , Mallikarjuna Rao Ch. , Singh Balpreet
Published: March 2024

The gig economy, characterized by its flexible work arrangements and independent contractors, has witnessed rapid growth in recent years. However, it faces sustainability challenges related to worker welfare, income security, and job classification. This paper explores the emerging role of artificial intelligence (AI) in the gig economy and its potential impact on these sustainability concerns. Through an analytical lens, it examines the current applications of AI in the gig economy across various platforms and tasks, highlighting both efficiencies and potential biases.The anticipated effects of AI on gig workers including job displacement, skill requirements, and potential for wage changes.The implications of AI for the sustainability of the gig economy, analyzing how it might exacerbate existing challenges or pave the way for improvements in worker protections and platform governance.Potential policy considerations and recommendations to ensure a more sustainable and equitable gig economy in light of AI integration. The paper concludes by emphasizing the need for a nuanced understanding of AI's potential in the gig economy, advocating for proactive measures to address potential disruptions and harness its capabilities to create a more sustainable and rewarding future for the flexible workforce.
Financing a Greener Future: Analyzing the Integral Role of Green Finance in Environmental Protection
Authors: Ganesh Lalisetti , Kusuma Sridevi , Radhakumari D. , Krishna P.V.V.S.S.R. , Pushkarna Gaurav
Published: March 2024

In order to integrate environmental preservation with economic rewards, a new financial pattern known as "green finance" has emerged. This pattern places an emphasis on the words "green" and "finance," both of which are politically charged topics. The purpose of this article is to investigate the current state of green finance in the banking sector and to identify certain deficiencies in the current system. Dedicated to The development of market mechanisms and the design of policies are both receiving attention. By drawing attention to the inherent inconsistencies that exist between green finance and environmental protection, we present solutions that are intrinsically suitable for achieving ecological balance in a more effective manner.
Gender diversity at workplace: Its implications on organizational performance at IT sector in Hyderabad
Authors: Pulusumamidi Neelima , Betgeri Sai Nethra , Parameshwari Chekuri Naga
Published: March 2024

The intellect and awareness of humans are thought to set them apart from one another. Studies conducted over the last few decades have demonstrated that women represent a substantial untapped reservoir of human capital. A growing number of competent women are entering fields that were previously thought to be exclusively male, thanks to the development of information technology and improved educational facilities. Because it is so important for businesses to draw in and keep workers in this fiercely competitive market, senior management has been paying enough attention to the creation of a women-friendly workplace. The bulk of research indicates that encouraging gender diversity has a beneficial impact on creativity and innovation. As a result, modern IT companies in India are implementing policies that guarantee a diverse workplace that is friendly to female employees. The aim of this paper is to examine the correlation between workplace assent of gender diversity, IT industry activities, and barriers to their implementation.
Smart Glove or Sign Language and AI-Driven Wheelchair Navigation
Authors: Akkineni Kalpana , Mounika Thodeti , Pooja Malgari , Reddy T. Bhanu Prasad , Reddy K. Sandeep , Ramesh G. , Gupta Gaurav
Published: March 2024

Gesture gloves are a promising solution for individuals who struggle with both mobility and communication. These gloves' sensor technology helps with nonverbal communication as well as mobility, which is especially beneficial for people who have trouble pushing manual wheelchairs[2]. The goal of gesture gloves, a notable development in assistive technology, is to empower people with disabilities by improving mobility and enabling effective communication through natural hand and finger movements[1]. The use of AI-controlled wheelchairs and smart gloves together raises the bar for assistive technology even higher. The smart glove uses flex sensors to read finger movements and translate them into messages for people who can't effectively use sign language. The AIcontrolled wheelchair simultaneously reacts to recognized gestures, removing the need for assistance from a person and providing an unprecedented degree of independence. For people with disabilities, this gesture-based communication and wheelchair control system offers a comprehensive and game-changing solution that seamlessly integrates into their everyday lives.
An ASIC Implementation: Wave-Pipelined Circuit for Dual-Tree Complex Wavelet Transformation
Authors: Mounika Nomula , Vandana Beemanpally
Published: March 2024

In digital systems, the wave-pipelining concepts are used to achieve maximal rate of operation. A high operating frequency are obtained by adjusting the two factors called clock skew, & clock period in order to latch the combinational logic circuit output as stable. In literature, only trial & error, manual procedures are used for the choice of the optimum clock period and clock skew values between input/outputs register of wave-pipelined circuit.The major contribution of this paper is to propose automated ASIC implementation using wave-pipelined circuits.to verify the proposal a dual-tree complex wavelet transformation algorithm is taken into consideration in which scheme is again carried out into 3 types i.e., Pipelining, wave-pipelining, non-pipeliningWith the implementation result survey, it is observed that wave -pipelined circuits are 30% more faster than that of non-pipelining circuits, pipelined circuits are 49% more faster than that of wave-pipelined circuits but occupies 30% more area and 3% more power dissipating than that of wave-pipelined circuits.
IoT based wifi fingerprint door lock system with raspberry pi & webcam
Authors: Shirisha P. , Satish Kumarr G. , Shivanjan K. , Shiva Rama Krishna K. , Vineela K.
Published: March 2024

The traditional doorlock system were facing lack of security with vital issues from burglary to overcome on this the latest technologies are being adopted to clear the gap of issues. In this system the IoT-based WiFi fingerprint door lock with live streaming using a Raspberry Pi and a webcam is a smart door lock system that provides enhanced security features for homes.The system utilizes the IoT platform for to integrate & connect the lock to a WiFi network and enable remote access through a mobile device.The system also features a Raspberry Pi device that is connected to a webcam, allowing live streaming of the entrance area. Users can view the live feed of the entrance area on their mobile application or whatapp , which providing an additional layer of security. The Raspberry Pi device can also be programmed to send notifications when the door is unlocked or opened, alerting the user of any unauthorized access.Using Raspberry Pi as the main platform for the project, allowing for easy customization and development. Integrating a webcam into the system to enable live streaming. The live steaming can be viewed from web Application or from whatsapp .
Stress Detection Based on Human Sleep Cycle
Authors: Papishetti Sathvika , Kain Balveer , Narayana Garlapati
Published: March 2024

Anxiety, mental or emotional stress resulting from a challenging environment, significantly impacts well-being. Understanding and monitoring human stress levels are crucial for averting adverse outcomes. This study investigates stress detection through machine learning and deep learning algorithms, focusing on sleep-related behaviours. Six machine learning techniques and deep learning methods, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deeper Neural Networks, were employed and compared against benchmarks from prior studies. Notably, the Naïve Bayes algorithm exhibited exceptional performance, achieving 91.27% accuracy. The integration of deep learning methods provided a broader perspective on stress detection and complemented insights from established studies. Leveraging previous research results not only served as benchmarks for our model but also validated and extended our understanding of stress detection based on sleep-related behaviours. Our findings contribute to the discourse on human stress monitoring.
MRI-based brain tumor detection and types of classification using CEHJB-RI and JMST
Authors: Kommanaboina Uma Shankar , Tummalsugur Sandeep , Kommanaboina Shirisha , Vijendar Reddy G. , Iurevna Usanova Kseniia
Published: March 2024

The Brain Tumor (BT), which forms in the brain cells and spreads to the whole brain, may lead to death. Hence, early diagnosis of BT is significant. Still, the detection of BT between the skull and brain region is not concentrated, which results in misclassification outcomes. Thus, this article proposes Magnetic Resonance Imaging (MRI)-based BT detection and types’ classification utilizing Carlitz Exponential Hamilton Jacobi Bellman-based Reinforcement Learning (CEHJB-RL) and JenSorensen similarity-based Minimum Spanning Tree (JMST). Primarily, raw MRI images are taken and then pre-processed. Then, with skull and without skull regions are extracted from the pre-processed image and are subjected to the graph construction. Conversely, the edges are detected from the pre-processed image that can be patch-extracted and subjected to graph construction. By utilizing JMST and Morphological Operations (MO), the graphs are constructed. Thereafter, the features are extracted and fed to the classifier. Then, the type of BT is classified by the classifier using CEHJB-RL. Concerning the performance metrics, the outcomes illustrated that the proposed technique attained a higher accuracy (99.27%), which is better than other existing techniques.
Enhancing Agricultural Productivity through Smart Farming with IoT
Authors: Sai Krishna Kudakala , Shravani Dupati , Shravan Macha , Shiva Chandra Prasad Reddy Tangella , Naresh Boyini , Fatima Nasra , Kiran Ravi , Mittal Aman
Published: March 2024

Technology has greatly advanced the agriculture industry, making it more data- driven and intelligent. The Internet of Things has grown at an exponential rate, causing significant changes in many industries, radical change creates opportunities as well as challenges because it destroys current farming methods. The Internet of Things (IoT) and wireless sensors' possible uses in agriculture are covered in this article. It also discusses some of the challenges that could occur from integrating this technology with traditional farming methods. The IoT tools and techniques used in agricultural applications are explained in detail. Based on this article, we can determine current IoT trends in agriculture.
The Evaluation of 2D and EfficientB0 Convolution Networks for detecting Brain tumor based on MRI images
Authors: Subbarayudu Yerragudipadu , Vijendar Reddy Gurram , Keerthi Dasari , Javeed Shaik Munazzah , Nagini R.V.S.S. , Bhardwaj Nitin
Published: March 2024

Brain tumors represent a significant healthcare challenge, affecting both children and adults with potentially aggressive consequences. Accounting for a substantial percentage of all primary Central Nervous System (CNS) tumors, brain tumors pose a substantial burden, with approximately 11,700 new diagnoses annually. The classification of brain tumors into benign, malignant, pituitary, and other types necessitates precise diagnostic techniques and treatment planning to enhance patient life expectancy. Traditionally, the detection of brain tumors relied on the expertise of specialists analyzing Magnetic Resonance Images (MRI) without the aid of advanced technology. MRI remains the gold standard for brain tumor detection, generating vast amounts of image data for radiologists to interpret. Manual examinations, however, carry a risk of errors due to the intricacies and diverse properties of brain tumors, potentially leading to delayed treatment and, tragically, loss of lives. In this context, the application of automated classification techniques using Machine Learning (ML) and Deep Learning (DL) has emerged as a promising solution. These techniques, primarily employing Deep Learning Algorithms such as Convolutional Neural Networks (CNN) like 2D-convolutions and Deep Learning Models like ResNet50 and EfficientNetB0, in addition to traditional Machine Learning algorithms like Support Vector Machines (SVM), have consistently demonstrated superior accuracy in brain tumor detection compared to manual prediction. These automated methods have consistently exhibited superior accuracy in the detection and classification of brain tumors compared to manual approaches. This research proposes a robust system for the early detection and accurate classification of brain tumors, leveraging the power of Deep Learning and Machine Learning. By incorporating state-of-the-art techniques, this system aims to empower medical professionals worldwide in identifying brain tumors at their earliest stages, ultimately leading to more timely and effective treatments. Such advancements hold great promise in reducing the human suffering associated with brain tumors and improving patient outcomes.
Detection and Classification of MRI Images using Multistage Classifier for Early Prediction of Alzheimer’s Disease
Authors: Venkateswara Reddy L. , Nagabhushana Rao M.
Published: March 2024

The term “neuro imaging” describes a set of procedures used for studying, diagnosing, and treating neurological disorders as well as for basic scientific research into the human brain. In order for medical professionals to make early diagnoses for their patients, they must investigate the signs of various neuro imaging kinds; among the most important elements in this process is the problem of neuro image classification. No effective treatment has been found for Alzheimer's disease (AD), a degenerative brain ailment that has no known cure. It is not possible to halt the progression of the disease once it has begun. But there are medications that can slow it down. Nevertheless, with the use of sophisticated prediction, the disease-influencing protein functions can be reduced. Because of shared neural networks and pixel strength, Alzheimer's disease diagnosis in the elderly is discretely challenging and calls for the portrayal of a discriminating element separately. Several writers attempted to apply machine learning to make early disease diagnoses, but they were unable to get reliable classification accuracy. To that end, we set out to develop a model that would combine deep neural networks with multistage classifiers in the hopes that it would be able to efficiently and effectively extract characteristics from input data. Better and more effective detection and classification of Alzheimer's disease was achieved in this study by employing a multistage classifier that made use of deep learning techniques. Using a benchmark database supplied by the Alzheimer's Neuro Imaging Institute, the suggested strategy achieves better outcomes than individual techniques.
Image encryption using enhanced DCT transform for frequency domain applications
Authors: Ganesh Bande , Ganesh Davanam , Sunil Kumar M. , Rishitha Pasupula , Sri Vidya Devi P. , Singh Balpreet
Published: March 2024

improving system performance and reliability. Key leaks and inadequate key strength are common issues with numerical global key models and public block cipher algorithms that use pseudo-random numbers. Additionally, because of the spatial correlation between neighboring pixel values, no current crypto scheme is appropriate for picture encryption. An intellectual domain-based image cryptosystem with hierarchical modification structures is proposed to overcome all these obstacles. The suggested framework also aims to maximize confusion metrics and improve security efficiency. In this paper modified DCT transform domain based frequency domain image encryption is proposed along with optimized pixel grouping based cipher key formulation with reduced complexity overhead. The proposed image encryption model can achieve 100 percentage transformations irrespective to the image non-linear characteristics and associated spatial dimensions. To validate the performance metrics of proposed image encryption different biomedical images collected and the superiority of proposed system is verified using quantitative and qualitative analyzes. The proposed implementation helps in offering privacy in crypto system based network security. It also validated that this hybrid combination also extend the applicability of crypto system for EMR applications.
OCR for devanagari script
Authors: Singh Mitali , Gayathri Pranava , Kandukuri Prabhakar
Published: March 2024

The introduction of Optical Character Recognition (OCR) technology revolutionized text digitization, allowing physical documents to be converted into editable and searchable digital representations. This paper goes into the unique challenges and advancements in OCR designed exclusively for the Devanagari script. It helps to preserve cultural heritage by digitizing historic manuscripts and religious writings and making them more accessible. Furthermore, Devanagari OCR has practical uses in administrative activities, data entry, and educational content digitization.