bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between production and reproduction parameters based on parity and breed of dairy cows in the Czech Republic
Authors: Vrhel Marek , Ducháček Jaromír , Gašparík Matúš , Vacek Mojmír , Codl Radim , Pytlík Jan
Published: April 2024

Abstract. Milk production and the efficiency of dairy cow breeding are significantly influenced by reproductive factors. The purpose of our research was to examine the relationships between selected milk production and reproductive parameters. We evaluated 659 dairy cows, including 444 purebreds from the Czech Fleckvieh and Holstein breeds and 215 crossbreds. Our primary objective was to assess the impacts of breed and parity on specific milk production and reproductive parameters. The study revealed significant results regarding the interaction between certain breed groups and parity. In particular, there was a noticeable increase in milk yield with parity. Furthermore, it was also evident that the highest milk yield values were related to the milk content. Breed group H, which represents cows with a more than 50 % Holstein bloodline, had the highest values of the monitored milk content. Our findings show that first-lactation crossbred Czech Fleckvieh cows had a reduced milk yield, decreased fat, and lactose content in milk. However, they had a more favourable calving interval when compared to purebred Czech Fleckvieh and higher-parity Holstein crossbreds. Crossbred H, in comparison to C100 and C50, whether purebred or crossbred with Czech Fleckvieh, showed a relatively higher content of some milk components. The results for service periods and calving intervals were not statistically significant. The findings of this study highlight the promising potential of higher-parity Holstein crossbred cows in terms of milk yield and the advantages of lower-parity ones concerning milk contents.
Validation of Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) chlorodifluoromethane (HCFC-22) in the upper troposphere and lower stratosphere
Authors: Kolonjari Felicia , Sheese Patrick E. , Walker Kaley A. , Boone Chris D. , Plummer David A. , Engel Andreas , Montzka Stephen A. , Oram David E. , Schuck Tanja , Stiller Gabriele P. , Toon Geoffrey C.
Published: April 2024

Abstract. The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) is currently providing the only measurements of vertically resolved chlorodifluoromethane (HCFC-22) from space. This study assesses the ACE-FTS HCFC-22 v5.2 product in the upper troposphere and lower stratosphere, as well as simulations of HCFC-22 from a 39-year specified dynamics run of the Canadian Middle Atmosphere Model (CMAM39) in the same region. In general, ACE-FTS HCFC-22 observations tend to agree with subsampled CMAM39 data to within ±5 %, except for between ∼ 15 and 25 km in the extratropical regions where ACE-FTS exhibits a negative bias of 5 %–30 % and near 6 km in the tropics where ACE-FTS exhibits a bias of −15 %. When comparing against correlative satellite, aircraft, and balloon data, ACE-FTS typically exhibits a low bias on the order of 0 %–10 % between ∼ 5 and 15 km and is within ±15 % between ∼ 15 and 25 km. ACE-FTS, CMAM39, and surface flask measurements from the NOAA Global Monitoring Laboratory's surface air-sampling network all exhibit consistent tropospheric HCFC-22 trends ranging between 6.8 and 7.8 ppt yr−1 (within 95 % confidence) for 2004–2012 and between 3.1 and 4.7 ppt yr−1 (within 95 % confidence) for 2012–2018. Interhemispheric differences (IHDs) of HCFC-22 were also derived using ACE-FTS, NOAA, and CMAM39 data, and all three yielded consistent and correlated (r≥0.42) IHD time series, with the results indicating that surface IHD values decreased at a rate of 2.2 ± 1.1 ppt per decade between 2004 and 2018.
How to observe the small-scale spatial distribution of surface solar irradiance, and how is it influenced by cumulus clouds?
Authors: He Zili , Libois Quentin , Villefranque Najda , Deneke Hartwig , Witthuhn Jonas , Couvreux Fleur
Published: April 2024

Abstract. The amount of solar radiation reaching the Earth surface (SSI) is critical for a variety of applications, ranging from surface-atmosphere interactions to solar energy. SSI is characterized by a large spatiotemporal variability, in particular in the presence of broken clouds. This results in complex spatial patterns of shadows and sunlight directly related to clouds' geometry and physical properties. Although key in many respects, the instantaneous spatial distribution of SSI remains largely unexplored. Here, we use unique observations from a dense network of pyranometers deployed during the HOPE field campaign to investigate the SSI spatial distribution. For cumulus scenes, bimodal distributions are found, with one mode corresponding to cloud shadows and the other to sunlit areas with enhanced SSI exceeding clear-sky values. Combining large-eddy simulations of cumulus clouds with Monte Carlo ray tracing, we demonstrate the capability of advanced numerical tools to reproduce the observed distributions and quantify the impact of cloud geometrical and physical properties on both modes. In particular, cloud cover strongly modulates their amplitudes, in addition to their location and width, which are also sensitive to cloud height, geometrical depth, and liquid water content. Combining observations and simulations, we propose sampling strategies to estimate the instantaneous spatial distribution of SSI with a limited number of sensors, highlighting that 10 pyranometers integrated over 10 min can capture most details of the full distribution. Such a strategy could be used for future campaigns to further investigate SSI distributions and their impact on land-atmosphere exchanges or PV farm management.
A quantitative module of avalanche hazard—comparing forecaster assessments of storm and persistent slab avalanche problems with information derived from distributed snowpack simulations
Authors: Herla Florian , Haegeli Pascal , Horton Simon , Mair Patrick
Published: April 2024

Abstract. Avalanche forecasting is a human judgment process with the goal of describing the nature and severity of avalanche hazard based on the concept of distinct avalanche problems. Snowpack simulations can help improve forecast consistency and quality by extending qualitative frameworks of avalanche hazard with quantitative links between weather, snowpack, and hazard characteristics. Building on existing research on modeling avalanche problem information, we present the first spatial modeling framework for extracting the characteristics of storm and persistent slab avalanche problems from distributed snowpack simulations. Grouping of simulated layers based on regional burial dates allows us to track them across space and time and calculate insightful spatial distributions of avalanche problem characteristics. We applied our approach to ten winter seasons in Glacier National Park, Canada, and compared the numerical predictions to human hazard assessments. Despite good agreement in the seasonal summary statistics, the comparison of the daily assessments of avalanche problems revealed considerable differences between the two data sources. The best agreements were found in the presence and absence of storm slab avalanche problems and the likelihood and expected size assessments of persistent slab avalanche problems. Even though we are unable to conclusively determine whether the human or model data set represents reality more accurately when they disagree, our analysis indicates that the current model predictions can add value to the forecasting process by offering an independent perspective. For example, the numerical predictions can provide a valuable tool for assisting avalanche forecasters in the difficult decision to remove persistent slab avalanche problems. The value of the spatial approach is further highlighted by the observation that avalanche danger ratings were better explained by a combination of various percentiles of simulated instability and failure depth than by simple averages or proportions. Our study contributes to a growing body of research that aims to enhance the operational value of snowpack simulations and provides insight into how snowpack simulations can help address some of the operational challenges of human avalanche hazard assessments.
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
Authors: Harrop Bryce E. , Lu Jian , Leung L. Ruby , Lau William K. M. , Kim Kyu-Myong , Medeiros Brian , Soden Brian J. , Vecchi Gabriel A. , Zhang Bosong , Singh Balwinder
Published: April 2024

Abstract. The interaction between clouds and radiation is a key process within the climate system, and assessing the impacts of that interaction provides valuable insights into both the present-day climate and future projections. Many modeling experiments have been designed over the years to probe the impact of the cloud radiative effect (CRE) on the climate, including those that seek to disrupt the mean CRE effect and those that only disrupt the covariance of the CRE with the circulation. Seven such experimental designs have been added to the Energy Exascale Earth System Model version 1 (E3SMv1) of the US Department of Energy. These experiments include both the first and second iterations of the Clouds On/Off Klimate Intercomparison Experiment (COOKIE) experimental design, as well as the cloud-locking method. This paper documents the code changes necessary to implement such experiments and also provides detailed instructions for how to run them. Analyses across experiment types provide valuable insights and confirm the findings of prior studies, including the role of cloud radiative heating toward intensifying the monsoon, intensifying rain rates, and poleward expansion of the general circulation owing to cloud feedbacks.
A modular library for fast prototyping of solution-state nuclear magnetic resonance experiments
Authors: Górka Michał , Koźmiński Wiktor
Published: April 2024

Abstract. We present a framework library (Modular Elements, ME) for the development of pulse sequences for Bruker spectrometers. It implements a two-level abstraction approach – the lower level comprises basic functional elements of pulse sequences and the higher one often-reused blocks comprising several evolution periods. The low-level abstractions reduce code duplication between variants of experiments such as hard-pulse and selective variants of individual NMR experiments. The high-level modules enable further reuse of pulse program code and aid in the construction of complex experiments. We show the library's functionality by presenting pulse programs that can be switched between standard and TROSY variants as well as hard and shaped pulses and that can seamlessly incorporate real-time homodecoupling. Adaptability is further demonstrated in a configurable 4D NOESY program.
Assessment of wind–damage relations for Norway using 36 years of daily insurance data
Authors: Jaison Ashbin , Sorteberg Asgeir , Michel Clio , Breivik Øyvind
Published: April 2024

Abstract. Extreme winds are by far the largest contributor to Norway’s insurance claims related to natural hazards. The predictive skills of four different damage functions are assessed for Norway at the municipality and national levels on daily and annual temporal scales using municipality-level insurance data and the high-resolution Norwegian hindcast (NORA3) wind speed data for the period 1985–2020. Special attention is given to extreme damaging events and occurrence probabilities of wind-speed-induced damage. Because of the complex topography of Norway and the resulting high heterogeneity of the population density, the wind speed is weighted with the population. The largest per capita losses and severe damage occur most frequently in the western municipalities of Norway, which are more exposed to incoming storms from the North Atlantic, whilst there are seldom any large losses further inland. There is no single damage function that outperforms others. However, a good agreement between the observed and estimated losses at municipality and national levels for a combination of damage functions suggests their usability in estimating severe damage associated with windstorms. Furthermore, the damage functions are able to successfully reconstruct the geographical pattern of losses caused by extreme windstorms with a high degree of correlation. From event occurrence probabilities, the present study devises a damage classifier that exhibits some skill at distinguishing between daily damaging and non-damaging events at the municipality level. While large-loss events are well captured, the skewness and zero inflation of the loss data greatly reduce the quality of both the damage functions and the classifier for moderate- and weak-loss events.
Transformation of internal solitary waves at the edge of ice cover
Authors: Terletska Kateryna , Maderich Vladimir , Tobisch Elena
Published: April 2024

Abstract. Internal wave-driven mixing is an important factor in the balance of heat and salt fluxes in the polar regions of the ocean. Transformation of internal waves at the edge of the ice cover can enhance the mixing and melting of ice in the Arctic Ocean and Antarctica. In the polar oceans, internal solitary waves (ISWs) are generated by various sources, including tidal currents over bottom topography, the interaction of ice keels with tides, time-varying winds, vortices, and lee waves. In this study, a numerical investigation of the transformation of ISWs propagating from open water in the stratified sea under the edge of the ice cover is carried out to compare the depression ISW transformation and loss of energy on smooth ice surfaces, including those on the ice shelf and glacier outlets, with the processes beneath the ridged underside of the ice. They were carried out using a non-hydrostatic model that is based on the Reynolds-averaged Navier–Stokes equations in the Boussinesq approximation for a continuously stratified fluid. The Smagorinsky turbulence model extended for stratified fluid was used to describe the small-scale turbulent mixing explicitly. Two series of numerical experiments were carried out in an idealized 2D setup. The first series aimed to study the processes of the ISWs of depression transformation under an ice cover of constant submerged ice thickness. Energy loss was estimated based on a budget of depth-integrated pseudoenergy before and after the wave transformation. The transformation of ISWs of depressions is controlled by the blocking parameter β, which is the ratio of the minimum thickness of the upper layer under the ice cover to the incident wave amplitude. The energy loss was relatively small for large positive and large negative values of β. The maximal value of energy loss was about 38 %, and it was reached at β≈0 for ISWs. In the second series of experiments, a number of keels were located on the underside of the constant-thickness ice layer. The ISW transformation under ridged ice also depends on the blocking parameter β. For large keels (β<0), more than 40 % of energy is lost on the first keel, while for relatively small keels (β>0.3), the losses on the first keel are less than 6 %. Energy losses due to all keels depend on the distance between them, which is characterized by the parameter μ, i.e. the ratio of keel depth to the distance between keels. In turn, for a finite length of the ice layer, the distance between keels depends on the keel quantity.
Evolution of small-scale turbulence at large Richardson numbers
Authors: Ostrovsky Lev , Soustova Irina , Troitskaya Yuliya , Gladskikh Daria
Published: April 2024

Abstract. The theory of stratified turbulent flow developed earlier by the authors is applied to data from different areas of the ocean. It is shown that turbulence can be amplified and supported even at large gradient Richardson numbers. The cause of that is the exchange between kinetic and potential energies of turbulence. Using the profiles of Brunt–Väisälä frequency and vertical current shear given in Forryan et al. (2013), the profiles of the kinetic energy dissipation rate are calculated. The results are in reasonable agreement with the experimental data.
Millennial-scale fluctuations of palaeo-ice margin at the southern fringe of the last Fennoscandian Ice Sheet
Authors: Tylmann Karol , Wysota Wojciech , Rinterknecht Vincent , Moska Piotr , Bielicka-Giełdoń Aleksandra ,   ASTER Team
Published: April 2024

Abstract. The paper presents the first terrestrial record of millennial-scale palaeo-ice margin oscillations at the southern fringe of the last Fennoscandian Ice Sheet (FIS) during the last glacial cycle. The study area is located in northern Poland close to the last FIS maximum limit. The chronology and dynamics of palaeo-ice margin oscillations at the southern fringe of the FIS are based on combined luminescence and 10Be surface exposure dating. Optically stimulated luminescence (OSL) was used to date sandy deposits (fluvioglacial sediments and aeolian deposits filling fossil periglacial wedges) intercalating basal till layers. The most likely age of the tills was constrained by Bayesian modelling of the sequence of OSL ages and lithostratigraphy. 10Be surface exposure dating was used on erratic boulders left during the final retreat of the last FIS and resting on the surface of glacial landforms. Our results, which are mainly based on OSL chronology and Bayesian modelling, indicate millennial-scale oscillations of the last FIS in northern Poland between ∼19 and ∼17 ka. The last FIS retreated and re-advanced over a relatively short period of time (2–3 ka), leaving lithostratigraphic records (basal tills) of three ice re-advances over a millennial-scale cycle: 19.2±1.1, 17.8±0.5 and 16.9±0.5 ka. Despite 10Be surface exposure ages obtained for 14 erratic boulders being poorly clustered, the main mode of age distribution occurs at ∼18 ka and indicates a possible signal of the ice sheet retreat after one of the re-advances. We explore the dynamics of these oscillations and compare the proposed cycles of the southern FIS advances and retreats with existing patterns of the last deglaciation and millennial-scale fluctuations of the last FIS inferred from marine records.
Pengujian Las SMAW Pada Rangka Engine Trainer Mitsubishi Canter 110 PS
Authors: Wilujeng Auliana , Hadiwijaya Lukman , Fatah Misbakhul , Fikri Mohammad Anas , Zakhi Ahmad Nur
Published: April 2024

Engine trainer merupakan alat bantu untuk menopang suatu mesin tertentu agar memudahkan operator dalam melakukan pekerjaan perawatan. Jurusan Teknik Mesin Alat Berat di Politeknik Negeri Madura dalam kegiatan belajar mengajarnya juga melakukan kegiatan perawatan. Adanya alat bantu engine trainer tentunya mempermudah mahasiswa dan dosen dalam mengakses bagian-bagian mesin. Pembuatan rangka engine trainer tentunya harus kokoh untuk dapat menopang mesin Mitsubishi Canter 110PS yang bobotnya mencapai 400 kg. Sambungan yang digunakan dalam penelitian ini adalah sambungan las. Metode pengelasan yang digunakan adalah metode las SMAW. Analisis pengaruh variasi arus dan electroda dilakukan dalam penelitian ini untuk mengetahui kekuatan pengelasan dan cacat permukaan yang terjadi karena adanya las. Variasi arus yang digunakan adalah 70 A, 80 A dan 90 A. Sedangkan variasi elektroda yang digunakan adalah E6013 dan E7016. Hasil yang didapat dari penelitian ini adalah tidak ada cacat permukaan setelah dilakukan pengelasan. Selain itu didapatkan tegangan bending terbesar pada variasi elektroda E6013 dengan kuat arus 90 A sebesar 35,52 MPa.
PHYSICOCHEMICAL AND MICROBIAL ANALYSIS OF BOREHOLE WATER IN HOSPITALS AROUND BENIN CITY, NIGERIA
Authors: ORDIA M.K. , SAIDU J.Z.
Published: April 2024

Ground water is known to be the major source of water in many hospitals, extracted through domestic boreholes for public and private usage. This study was carried out to access the physicochemical and bacteriological quality of borehole water in some hospitals in Benin City. Borehole water samples were collected from three different hospitals (Hospital A, Hospital B and Hospital C) around Benin City, Edo State into sterile plastic containers to avoid additional contamination before being taken to the laboratory for physicochemical and bacteriological assessment. The results showed the presence of microorganisms in the water samples. The highest bacteria heterotrophic counts were seen in hospital C (81.0±0.5), while the lowest was observed in hospital B (7.0±0.5). The probable identified bacterial isolates were Salmonella sp., Staphylococcus sp., Pseudomonas sp., Bacillus sp., Shigella sp., Klebsiella sp. and Escherichia coli. The Physicochemical analysis revealed that the highest pH value (6.06±0.33) was observed in water from hospital A, total suspended solids (1.30±0.08 mg/ml), turbidity (2.73±0.11 NTU), nitrate (1.05±0.05 mg/ml), phosphate (0.23±0.00 mg/ml), sulfate (1.84 mg/ml), biological oxygen demand (0.03±0.01 mg/ml) and chemical oxygen demand (0.81±0.09 mg/ml) were also recorded. No heavy metals were found from the water samples within the three hospitals. This study revealed the occurrence of microorganisms, hence the need for water treatment in hospitals. Environmental agencies should also ensure compliance with relevant standards to avoid risks to human health.
The Movement Design of Dolls in the Western Region in the Exhibition Art Life and Stage
Authors: Zhou Quan
Published: April 2024

Stage art is a combination of stage music, stage art, literature and other art forms, and it is a form of artistic expression that resonates with the audience by conveying certain ideas. With the widespread use of digital economy in many fields of life and the transformation of society, the border between stage art and life is gradually blurred, and the art form has become multi-dimensional and diversified, and everyone is in the art, enjoying the charm of art itself. Where, the design of the movement of dolls on stage has a more important role, this paper on the exhibition of art life and stage art, and combined with the actual case study to illustrate the effectiveness of this paper on the design of the movement of dolls. The experimental results show that the western region ancient style puppet action design method in this paper has a significant effect on improving the performance ability of the art stage, and brings a variety of inspiration to the puppet action design.
Marketing Strategy of Rural Home Stay Industry Based on Intelligent IOT
Authors: Kangkang Zhang Jing Hu,
Published: April 2024

The concept of modern tourism intelligent application in China is to fully meet the information needs of tourists and customer experience, and the development of intelligent tourism relying on Internet of Things (IoT) is its most basic guarantee condition. With the rise of ethnic village tourism and the change of new consumer lifestyle, B&B shows an explosive development. Tourism consumers are also shifting from hotels to home stays in the process of tourism. Therefore, based on IOT application as the starting point, this paper firstly, gives a more detailed description of the development process and marketing status of home stays, and discusses its various problems. Then, based on the TP theory of ethnic villages, Porter's five forces analysis model and the SWOT analysis method of ethnic villages, the marketing environment of homestays is studied in depth to discover their advantages.The HTOE model develops a marketing mix strategy for B&Bs that meets the actual situation of ethnic village development.
Problem Analysis and Application Research in Legal Judgment Prediction Based on Data Mining
Authors: Si Tianyuan
Published: April 2024

The judgments of some hot cases that exceeded public expectations seem to confirm this. Although the legal certainty is questioned, the judgment remains predictable. The predictability of judgments is a way to enhance judicial authority and maintain judicial credibility. It is also a way to achieve overlapping consensus between the judiciary and the public, provide stable value guidance and behavioral expectations for the public, and promote the generation and development of public rational trust. To enhance public legal trust by improving the predictability of judgments, it is necessary to increase the burden of reasoning for judicial judgments, avoid randomness and contingency, and ensure the adequate provision and substantial disclosure of previous judgment information, so that judgment prediction in the era of big data can truly become possible. The research is to use the optimized particle swarm algorithm as the underlying model to carry out joint modeling and prediction research on the analysis and application of problems in the prediction of legal judgments. According to experimental calculations, the optimized particle swarm algorithm can significantly improve the accuracy and universality of legal judgment prediction. After optimization, the convergence speed is increased by about 5%, and the value of the acceleration factor is more significant.
The Effect of Age, Working Period and Workload on Work Productivity Through work Fatigue and Sleep Quality at Employees of Pt. Sarana Usaha Sejahtera Insanpalapa (Telkomedika)
Authors: Lalu Muhammad Saleh , Syamsiar R Russeng Dian Pratiwi Abdullah ,
Published: April 2024

Workload that exceeds one's ability, will cause work fatigue, which will disrupt the sleep process. If the sleep process is disrupted, the expected quality of sleep will not be achieved, which will have an impact on work productivity. This study aims to determine the direct and indirect effects of workload on work productivity through work fatigue and sleep quality.

This research is an analytic observational study with a cross-sectional study design and analysis using SPSS and AMOS to determine the path analysis of each variable. This research was conducted at PT Sarana Usaha Sejahtera InsanPalapa (TelkoMedika) with a total sample of 119 people.

The results showed that there was a direct effect between workload and work productivity with a p-value of 0.003 and a contribution of -0.356, which means that there is a negative relationship, namely that the higher a person's workload, the lower his work productivity, and vice versa. And there is also an indirect effect of workload on work productivity through work fatigue, with a p value of 0.012. Companies should pay attention to the workload given to employees because it will affect their health and productivity at work.
A Proposed Framework of VAPT Services in Web Application Deployed on Infrastructure as a Service (Iaas)
Authors: Noraida Haji Ali
Published: April 2024

Most companies in Malaysia require their employees to work from home due to the COVID-19 pandemic. This situation also increased the number of data generated from various sources, thus exposing them to different security risks. Even though the employees are encouraged to work from home because of the COVID-19 pandemic, they still need to communicate among themselves to do their work. However, working from home depends mainly on cloud computing (CC) applications that help employees accomplish their daily work efficiently. Injection attacks, such as SQL injection and Cross-Site Scripting (XSS), are critical security vulnerabilities that can lead to unauthorized access, data breaches, and potential service disruptions in web applications. With the increasing adoption of cloud computing, web applications deployed on cloud platforms like Amazon Web Services (AWS) are becoming more prevalent and vulnerable to such attacks. Therefore, it is crucial to develop practical Vulnerability Assessment and Penetration Testing (VAPT) techniques specifically tailored to identify and detect injection vulnerabilities in web applications deployed on AWS. However, existing VAPT methodologies often need more comprehensive coverage for injection vulnerabilities in cloud-based web applications, and they may not consider the unique characteristics and challenges associated with the AWS environment. This research addresses this gap by proposing an enhanced VAPT framework focusing specifically on injection attacks in web applications deployed on AWS.
Efficient Disease Prediction Framework to Suggest Early Treatment Decisions in Healthcare
Authors: Yusuf Abubakar , Khalid Hussain Halawati Abd Jalil Safuan
Published: April 2024

This study proposes a novel framework for disease prediction intended to aid healthcare providers in making early treatment decisions. In order to efficiently identify early disease indicators and provide prompt suggestions for treatment interventions, the framework incorporates machine learning algorithms, clinical data analysis, and predictive modeling techniques (Smith, Johnson, & Davis, 2023). For the purpose of creating the framework, a comprehensive dataset made up of clinical records, demographic data, and test results from a large patient cohort was gathered. The dataset was analyzed using a variety of machine learning methods, and predictive models for various diseases were created. As evaluation criteria, accuracy, precision, recall, and F1-score were used to gauge how well these models performed. The experimental findings show that the suggested framework has a high degree of disease prediction accuracy. Additionally, by recommending early treatment selections. The approach offers the potential to improve patient outcomes and lower healthcare expenditures based on anticipated illness outcomes. The study benefits healthcare professionals, policymakers, and academics looking to use data-driven strategies for better disease management and patient care by providing an effective method for disease prediction and early treatment decisions.
VEGSHELL
Authors: Snitha Shetty
Published: April 2024

The Agricultural sector provides employment to over two third of the population of our country. This project aims at building a community for the farmers, educating them about high margin crops, practices to follow increase their profitability. Every now and then we hearing about the pathetic condition about our farmers. And despite employing more than 50% of the workforce the condition of the Indian agricultural section has been very bad. Every time we listen to this, we curse the government and we just move on until another news comes in. And because of this shallow activity we never understand the problem of our farmers at the ground level. The consistent high growth of agriculture sector is a prerequisite for India to become a globally economic superpower. Our application has been designed in such a way that farmers with no much education could easily adapt to our application. During the time of green revolution to make India ‘food self-sufficient’ we started farming major crops like wheat, rice etc. Even today we continue the same. It requires a change; it is also needed to concentrate equally on crops with higher margin.
Educators’ Resilience Amidst Digital Era Challenges: Case Study in Indonesia
Authors: Safuri Musa
Published: April 2024

This study aims to explore the coping mechanisms and resilience strategies employed by educators in response to the multifarious challenges posed by the digital era in Indonesia. The research objective centers on identifying and analyzing the various adaptive measures educators implement to maintain efficacy and well-being amidst the evolving educational landscape. A qualitative approach with case study research method was employed, focusing on a diverse group of ten educators, each facing distinct challenges. These informants were selected to provide a broad perspective on the resilience strategies utilized in various educational contexts. Data were collected through in-depth interviews, non-participative observation, and document study ensuring a rich and nuanced understanding of the educators' experiences.  The analysis was conducted using a thematic approach, identifying recurring patterns and strategies across the informants' narratives. The research findings underscore the complexity of educators' resilience in the digital era. It reveals that educators' resilience is multifaceted, extending beyond mere technological adaptation. It encompasses a blend of emotional intelligence, interpersonal skills, and continuous learning, all of which are crucial in navigating the intricacies of modern educational environments. The study sheds light on the indispensable role of resilience and adaptability for educators in the digital age, providing valuable insights for educational institutions aiming to support their staff effectively.
Application of The Aggregation and Multicriteria Optimization Method in Assessing the Level of the Knowledge Economy of World Regions in Industry 4.0
Authors: Akhundov V.J
Published: April 2024

The article proposes a new methodology for determining the level of the knowledge economy of countries in terms of the possibilities of using Industry 4.0 technologies. The relevance of the study is due to the transition to a new technological structure – “Industry 4.0” in the world. The aim of the study is to analyze data on the main sub-indices of the knowledge economy associated with Industry 4.0 for countries around the world based on the author’s methodology. As a tool for this analysis, the k-means++ clustering method was used to classify countries according to their characteristics. The assessment of the potential of Industry 4.0 is determined on the basis of 5 criteria characterizing various aspects of the development of countries at the level of economic knowledge. In the calculation process, determining the extent to which the indicators used describe the level of the knowledge economy, and the use of the aggregation formula and multi-criteria optimization, constitutes the scientific novelty of the study. It was concluded that as a result of clustering the countries of the world using the formula Aggregation and Multicriteria Optimization, the number of clusters decreased and the distribution of countries between clusters began to more adequately reflect reality. The results of the study can be used in the process of assessing the competitiveness of countries during the transition to the Industry 4.0 economy, as well as in developing strategies and concepts for countries.
AI Value Creation: Operational value creation potential of semantic AI assistants in corporate training
Authors: Alica Sailer
Published: April 2024

This paper explores the potential value of semantic artificial intelligence (AI) in corporate training based on a study of semantic AI learning assistants in secondary schools and interviews with human resource professionals. The findings suggest that semantic AI can significantly improve learning outcomes and streamline corporate training processes through personalized, on-demand learning. However, successful implementation requires addressing privacy, security, and user acceptance challenges. While benefits such as efficiency, cost savings, and performance gains are achievable, value creation depends on the organizational context. Further research should explore the long-term impact of semantic AI in corporate learning.
The Effect of Marketing Communication, Risk and Trust on Online Shopping Behavior
Authors: Firdaus Yuni Dharta
Published: April 2024

This research was conducted to analyze the effect of marketing communications that were protected through website design, perception risk, trust, and shopping behavior online (survey of Singapore University students in Karawang as shop.co.id customers). The sampling technique used was purposive sampling. The number of respondents in this study was 200 people. The analysis technique used is Partial Least Square. The results of the analysis show that marketing communication is shown by the appearance of design web, and Trust influence on Shopping Behavior Online through shopee.co.id. At the same time, the perception of risk has a negative but not significant effect.
Benefits of therapeutic Yoga in reducing stress in the elderly
Authors: Evelyn Alina Anicama Navarrete
Published: April 2024

Therapeutic yoga or yogatherapy, has great benefits for people, since its practice allows to improve mood, emotional balance, quality of sleep, among others. Social isolation as a result of the pandemic caused by COV-SARS-2, led to cases of family violence increasing considerably, affecting people's mental health and increasing stress levels. The objective of the article was to determine the benefits of therapeutic Yoga in the reduction of stress in the elderly of the Center for the Elderly of Ica. The study was of quantitative approach, applied type, application level, quasi-experimental design, there was a sample consisting of the 28 older adults of the CAM, divided into an experimental group (14) and a control group (14). Characterization variables such as age, sex, marital status, cohabitation and others were analyzed; a questionnaire consisting of 20 reagents with Likert scale was used, as a result a p-value of 0.000 was obtained in the post-test test, demonstrating significant differences between the experimental group and the control group. It is concluded that the application of therapeutic yoga contributes to the reduction of stress in older adults.
Factor Influencing Student Performance: Organizational Culture, Transformational Leadership, and Student Engagement
Authors: Ika Nurul Qamari
Published: April 2024

We examine the factors influencing student performance in student organizations on private campuses in Indonesia by exploring the relationship between organizational culture and transformational leadership. However, the topic of student performance is still limited to research. Most research on organizational culture focuses on employee performance. This is a quantitative approach that is based on cross-sectional data. In total, 157 respondents were used to infer the results through the structural equation model. The results do not support the argument that organizational culture relations are based more on student engagement than student performance. Conversely, the results support the argument that transformational leadership relationships affect student performance and engagement. To address the possibility of endogeneity issues, we use a series of variables, including student engagement which mediates in this study.
Exploring the Impact of Some Types of Virtual Tours on Data Center Design Proficiency
Authors: Noha Mahmoud Ahmed Mahmoud Shipriaa
Published: April 2024

The use of virtual tours has garnered attention as a promising method to enhance skills in various domains, including data center design. This research delves into the influence of different virtual tour modalities on the enhancement of data center design proficiency among both students and professionals. Through a comprehensive series of experiments and surveys, we investigate the efficacy of panoramic and video-based virtual tours in augmenting participants' comprehension of data center design principles, their capacity to apply these principles practically, and their overall competency in crafting efficient and reliable data centers. Our findings demonstrate a significant enhancement in data center design proficiency facilitated by virtual tours, particularly immersive and interactive formats surpassing traditional methods. Furthermore, our study uncovers the role of individual variances, such as prior expertise and experience in data center design, in modulating the effectiveness of virtual tours. These insights carry substantial implications for the evolution of pedagogical strategies in data center design education and training, advocating for the integration of virtual tour technologies into established curricula.
The Effects of Blended learning on University Students’ Critical Thinking Dispositions in College English Course
Authors: Li Li, Shaharuddin Bin Md Salleh
Published: April 2024

This research aims to introduce and examine the effects of blended learning environment on learners’ critical thinking dispositions for undergraduate students of business major at North China University of Science and Technology (NCSTU) in China. The participants are 28 freshmen from College English Course. The research tools are Critical Thinking Dispositions Inventory Chinese Version and Blended Learning Environment Questionnaire. The findings show that blended learning environment is beneficial for improving Chinese students’ critical thinking dispositions (CTD). The students are willing to take part in the learning activities both online and offline. They find out it is most difficult part is to understand the teacher’s English instructions. Majority of students’ CTD is positive after one semester learning in the blended learning environment. Open-mindedness, self- confidence, and inquisitiveness of CTD are significantly correlated with students’ gender, English scores of college entrance exam, and science and liberal arts respectively
The Future Higher Education in Albania in the Digital Era–Challenges and Opportunities
Authors: Dr. Etleva Babameto, Dr. Doriana Pano
Published: April 2024

Over the last decade, Albania made genuine attempts to adopt its education system after the European model considering the implementation and integration of ICT as a functional skill of digital literacy to face, among others, the challenges of current globalization. But the Covid-19 crisis evidenced that such a revolutionizing of the education system was applied to ICT incorporation and implementation into a curriculum of merely conventionally-based teaching and learning process. Precisely, the research question addresses the evolution of the higher education in Albania against the backdrop of lessons learned from the pandemic crisis in view of an effective and qualitative education system while making the most of technological and digitalized tools. The study is based on a qualitative analysis offering a theoretical approach of primary and secondary sources and continues with the empirical research conducted in 2023 - a survey questionnaire regarding students’ perceptions of the blended mode of teaching and learning process and also a comparative assessment with the one conducted in 2020 regarding their attitudes of online delivery. Hence, by considering the analysis in two quite differing contexts, the aim is to explore how have students’ outlooks changed regarding the mode of delivery of the teaching and learning process since the pandemic period to date. The study also includes in-field expertise acquired to that aim from the University of Münster (Germany) in the framework of the RESILIENCE Project.
Review and Study of the Inhibiting Character of Coffee Husk Extract to Reduce Corrosion Rate in Stainless Steel-AISI 440 in 1M H2SO4 Acid Medium Validated by Techniques Electrochemical – EIS
Authors: Elvis Gilmar Gonzales-Condori Janeth Marlene Quispe-Avilés,
Published: April 2024

Corrosion problems in industrial sectors, it is necessary to use corrosion inhibitors, most of the compounds used in the manufacture of inhibitors are very toxic. The use of green corrosion inhibitors has been studied with great intensity for the development of sustainable economies that promote the care of the environment through ecological alternatives and of low cost. Plant extracts are biodegradable and represent a renewable source of chemical compounds. In this work the coffee husk was used as a corrosion inhibitor, the material studied was a stainless steel AISI 440 in 1M H2SO4 medium. EIS tests were performed to compare the inhibitory effect. The results showed that in fact, the addition of coffee extract in the solutions rich in sulfuric acid was able to inhibit pitting and localized corrosion in stainless steel.
The Effect of Moringa Leaf (Moringa oleifera L.) Liquid Organic Fertilizer with Different Concentrations on the Growth of Beans (Phaseolus vulgaris L.)
Authors: Leni Sri Mulyani, Rifaatul Muthmainnah Sri Mulyaningsih, Winia Sastia, Diah Ika Putri,
Published: April 2024

One of the efforts to increase the production of chickpea plants to meet market needs is to optimally utilize the waste of Moringa leaves by converting Moringa leaves into liquid organic fertilizer so that it is easier and more practical to apply and easily absorbed by plants. Liquid organic fertilizer plays a role in improving the physical, chemical and biological properties of the soil, which can increase the production of upright bean plants. This study aims to determine the effect of liquid organic fertilizer (POC) of Moringa leaves with different concentrations and to determine the most effective concentration of liquid organic fertilizer (POC) on the growth of green beans (Phaseolus vulgaris L.). The method used is an experimental method with a completely randomized design (CRD). The treatment consisted of a concentration of 16% chicken manure (as a control), POC concentrations of 60%, 65%, 70%, 75% and 80%. The results showed that there was an effect of different concentrations of liquid organic fertilizer (POC) from Moringa oleifera L. on the growth of plant height, number of leaves and wet weight in bean plants (Phaseolus vulgaris L.). The most effective concentration of liquid organic fertilizer (POC) from Moringa leaves is at a concentration of 80% with an average plant height of 41.2 cm, an average number of leaves of 15.25 leaves and an average wet weight of 3.98 grams. number of leaves and fresh weight on chickpea plants (Phaseolus vulgaris L.). The most effective concentration of liquid organic fertilizer (POC) from Moringa leaves is at a concentration of 80% with an average plant height of 41.2 cm, an average number of leaves of 15.25 leaves and an average wet weight of 3.98 grams. number of leaves and fresh weight on chickpea plants (Phaseolus vulgaris L.). The most effective concentration of liquid organic fertilizer (POC) from Moringa leaves is at a concentration of 80% with an average plant height of 41.2 cm, an average number of leaves of 15.25 leaves and an average wet weight of 3.98 grams.
Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review
Authors: Marwah K. Muttair
Published: April 2024

Breast cancer is a major public health concern, affecting millions of women worldwide. Good patient outcomes and a successful course of therapy depend on a prompt and accurate diagnosis. The application of deep learning algorithms to the breast cancer classification problem has yielded some encouraging results; this might open the way for more rapid and accurate diagnostics. This article examines the many deep learning (DL) approaches taken to date for BC classification tasks, outlining each one's strengths, weaknesses, and current challenges. This study discusses several DL algorithms like convolutional neural networks, multi-layer neural networks, and autoencoders that analyze histopathological pictures, mammograms, and other forms of medical imaging. Furthermore, we investigate the interpretability & explainability features of DL models as they pertain to BC diagnoses, drawing attention to the need for reliable decision-making tools for medical practitioners. Throughout the review, we identify challenges and potential biases in current research. Finally, we provide an outlook on the future directions of DL in BC classification, focusing on promising research areas. By highlighting the achievements and gaps in the existing literature, this review aims to inspire further advancements in DL-based BC diagnosis, eventually resulting in better healthcare and reliable diagnosis.