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This study aims to examine the phenomenon of climate variability and its implications for pastoralists and agro-pastoralists food security in Amibara and Awash Fentale districts of the Afar region, Ethiopia.

The study relied on meteorological records of temperature and rainfall in the study area between 1988 and 2018. Besides, literature on the topic was reviewed to make caveats on the literal picture that comes from quantitative data, and that is the contribution of this study to the existing debate on climate change and variability. The spatiotemporal trend was determined using the Mann–Kendall test and Sen’s slope estimator, while variability was analyzed using the coefficient of variation and standardized anomaly index, and standardized precipitation index/standardized precipitation evapotranspiration index were applied to determine the drought frequency and severity.

The result reveals that the mean seasonal rainfall varies from 111.34 mm to 518.74 mm. Although the maximum and minimum rainfall occurred in the summer and winter seasons, respectively, there has been a decrease in seasonal and annual at the rate of 2.51 mm per season and 4.12 mm per year, respectively. The study sites have been experiencing highly seasonal rainfall variability. The drought analysis result confirms that a total of nine agricultural droughts ranging from moderate to extreme years were observed. Overall, the seasonal and annual rainfall of the Amibara and Awash Fentale districts showed a decreasing trend with highly temporal variations of rainfall and ever-rising temperatures, and frequent drought events means the climate situation of the area could adversely affect pastoral and agro-pastoral households’ food security. However, analysis of data from secondary sources reveals that analyzing precipitation just based on the meteorological records of the study area would be misleading. That explains why flooding, rather than drought, is becoming the main source of catastrophe to pastoral and agro-pastoral livelihoods.

The analysis of temperature and rainfall dynamics in the Afar region, hence the inception of all development interventions, must take the hydrological impact of the neighboring regions which appears to be useful direction to future researchers.

The research is originally conducted using meteorological and existing literature, and hence, it is original. In this research, we utilized a standardized and appropriate methodology, resulting in insights that augment the existing body of knowledge within the field. These insights serve to advance scholarly discourse on the subject matter.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

This study aims to explore the dissemination and evolutionary path of online public opinion from a crisis management perspective. By clarifying the influencing factors and dynamic mechanisms of online public opinion dissemination, this study provides insights into attenuating the negative impact of online public opinion and creating a favorable ecological space for online public opinion.

This research employs bibliometric analysis and CiteSpace software to analyze 302 Chinese articles published from 2006 to 2023 in the China National Knowledge Infrastructure (CNKI) database and 276 English articles published from 1994 to 2023 in the Web of Science core set database. Through literature keyword clustering, co-citation analysis and burst terms analysis, this paper summarizes the core scientific research institutions, scholars, hot topics and evolutionary paths of online public opinion crisis management research from both Chinese and international academic communities.

The results show that the study of online public opinion crisis management in China and internationally is centered on the life cycle theory, which integrates knowledge from information, computer and system sciences. Although there are differences in political interaction and stage evolution, the overall evolutionary path is similar, and it develops dynamically in the “benign conflict” between the expansion of the research perspective and the gradual refinement of research granularity.

This study summarizes the research results of online public opinion crisis management from China and the international academic community and identifies current research hotspots and theoretical evolution paths. Future research can focus on deepening the basic theories of public opinion crisis management under the influence of frontier technologies, exploring the subjectivity and emotionality of web users using fine algorithms and promoting the international development of network public opinion crisis management theory through transnational comparison and international cooperation.

This study aims to use geographical information on social media for public opinion risk identification during a crisis.

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.

The domains of Industry 4.0 and Smart Farming encompass the application of digitization, automation, and data-driven decision-making principles to revolutionize conventional sectors. The intersection of these two fields has numerous opportunities for industry, society, science, technology and research. Relatively, this intersection is new, and still, many grey areas need to be identified. This research is a step toward identifying research areas and current trends.

The present study examines prevailing research patterns and prospective research prospects within Industry 4.0 and Smart Farming. This is accomplished by utilizing the Latent Dirichlet Allocation (LDA) methodology applied to the data procured from the Scopus database.

By examining the available literature extensively, the researchers have successfully discovered and developed three separate research questions. The questions mentioned above were afterward examined with great attention to detail after using LDA on the dataset. The paper highlights a notable finding on the lack of existing scholarly research in the examined combined field. The existing database consists of a restricted collection of 51 scholarly papers. Nevertheless, the forthcoming terrain harbors immense possibilities for exploration and offers a plethora of prospects for additional investigation and cerebral evaluation.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

The paper proposes that the incorporation of Industry 4.0 technology into agricultural operations can enhance efficiency, production and sustainability. Furthermore, it highlights the significance of creating user-friendly solutions specifically tailored for farmers and companies. The study indicates that the implementation of supportive legislative frameworks, incentive programmes and resource conservation methods might encourage the adoption of smart agricultural technologies, resulting in the adoption of more sustainable practices.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

Based on a thorough examination of existing literature, it has been established that there is a lack of research specifically focusing on the convergence of Industry 4.0 and Smart Farming. However, notable progress has been achieved in the field of seclusion. To date, the provided dataset has not been subjected to analysis using the LDA technique by any researcher.

Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health

We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented

Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions

The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop

The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.

This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature

With the rise of the gig economy, management positions are increasingly staffed with flexible labor, so-called interim managers. They plunge into organizations for a limited period, operating in a liminal position as partly insider, partly outsider. Although several contributions to their client organizations are acknowledged, it is unknown how the interim manager’s knowledge from previous assignments is made useful in the new context under these particular working conditions. Therefore, the purpose of this paper is to increase the understanding of how the interim manager’s knowledge is transferred to the client organization while operating from a liminal position.

This paper presents an interview-based multiple case study of six interim assignments where knowledge transfer is considered a social and context-dependent process.

The findings unveil the multifaceted nature of the liminal position, which consists of task orientation, time limitation, political detachment and cultural distance. These facets contribute to knowledge transfer in terms of new shared understandings and joint interests, which in turn might create new practices that augment continuous knowledge-sharing patterns.

The results contribute to the research on flexible work arrangements by shedding light on how the liminal position, predominantly depicted as an obstacle for the individual, might facilitate knowledge transfer. Through the process of knowledge generation, it is shown how a short-term engagement might enable the organization to increase its knowledge over time.

The study aims to assess the effectiveness of prevailing methods for quantifying physical climate risks. Its goal is to evaluate their utility in guiding financial decision-making within the real estate industry. Whilst climate risk has become a pivotal consideration in transaction and regulatory compliance, the existing tools for risk quantification frequently encounter criticism for their perceived lack of transparency and comparability.

We utilise a sequential exploratory mixed-methods analysis to integrate qualitative aspects of underlying tool characteristics with quantitative result divergence. In our qualitative analysis, we conduct interviews with companies providing risk quantification tools. We task these providers with quantifying the physical risk of a fictive pan-European real estate portfolio. Our approach involves an in-depth comparative analysis, hypothesis tests and regression to discern patterns in the variability of the results.

We observe significant variations in the quantification of physical risk for the pan-European portfolio, indicating limited utility for decision-making. The results highlight that variability is influenced by both the location of assets and the hazard. Identified reasons for discrepancies include differences in regional databases and models, variations in downscaling and corresponding scope, disparities in the definition of scores and systematic uncertainties.

The study assists market participants in comprehending both the quantification process and the implications associated with using tools for financial decision-making.

To our knowledge, this study presents the initial robust empirical evidence of variability in quantification outputs for physical risk within the real estate industry, coupled with an exploration of their underlying reasons.

The purpose of this study is to investigate environmental factors impacting Ajanta mural deterioration, assessing global tourism effects and visitor conduct on cave environment and murals. This study recognizes stakeholder roles in conservation, providing data-driven insights to guide institutions like the Archaeological Survey of India. The objective is sustainable tourism practices to balance public access with mural preservation for future generations.

Over 25 years, Ajanta Caves' visitors doubled, impacting microclimatic conditions for ancient murals. This study assesses visitor impact to establish a regime and determine carrying capacity, considering temperature, humidity and pollution. Challenges arise from quantifying capacity because of variables. This research informs global tourism and heritage conservation, offering methodologies applicable to cultural sites worldwide.

This study examines environmental impacts on mural preservation in Ajanta Caves, including humidity, microbial growth, sunlight exposure, air quality and tourist presence. Tourist influx escalates CO₂ levels, directly endangering murals. Concerns about particulate matter, especially during visits, emphasize the need for data-driven decision-making and modern technology use to protect Ajanta Caves' artwork, crucial because of its global significance and tourism-related vulnerabilities.

This study carries substantial social implications with a global resonance. The active engagement of the local community and tourism stakeholders in conserving and promoting the Ajanta Caves fosters empowerment, igniting a sense of pride, ownership and responsibility among residents and ensuring sustainable enjoyment of cultural heritage while safeguarding it for future generations. In addition, there will be socioeconomic benefits to local residents such as employment opportunities as tour operators, tour guides, hospitality staff, artisans and souvenir shopkeepers.

This study integrates art conservation, environmental science, cultural heritage preservation and social aspects to address global tourism challenges. Focusing on a site of worldwide significance, this study offers practical strategies for artwork preservation, tourism management and environmental concerns. These recommendations provide real-world solutions applicable to heritage sites globally, bridging scientific analysis with social and cultural insights.

本研究调查了影响阿詹塔壁画退化的环境因素, 评估了全球旅游业和游客行为对洞穴环境和壁画的影响。它承认利益相关者在保护中的作用, 为印度考古调查等机构提供数据支撑的见解。目的是实践可持续旅游, 平衡公众进入和供子孙后代使用的壁画保护。

25年来, 阿詹塔洞穴的游客增加了一倍, 影响了古壁画的小气候条件。该研究评估了游客的影响, 考虑温度、湿度和污染, 建立了一个制度并确定承载能力。量化承载能力的指标面临着挑战。这项研究为全球旅游业和遗产保护提供了信息, 提供了适用于世界各地文化遗址的方法。

这项研究考察了环境对阿詹塔洞穴壁画保护的影响, 包括湿度、微生物生长、阳光照射、空气质量和游客的存在。游客的涌入使二氧化碳含量上升, 直接危及壁画。对颗粒物的考虑, 尤其是在参观期间, 强调了数据驱动决策和现代技术应用对保护阿詹塔洞穴的艺术品的必要性。这一点至关重要, 因为它具有全球意义和旅游相关的脆弱性。

这项研究具有重大的社会影响并引起全球共鸣。当地社区和旅游利益相关者积极参与阿旃陀石窟的保护和推广, 可以增强居民的赋权, 激发居民的自豪感、主人翁意识和责任感, 确保可持续享受文化遗产, 同时为子孙后代保护文化遗产。此外, 还将为当地居民带来社会经济效益, 例如旅游经营者、导游、接待人员、工匠、纪念品店主等的就业机会。

这项研究综合了艺术保护、环境科学、文化遗产保护和社会方面以应对全球旅游业的挑战。它关注一个具有世界意义的遗址, 为艺术品保护、旅游管理和环境问题提供了实用的策略。这些建议提供了适用于全球遗产地的现实世界解决方案, 将科学分析与社会和文化见解联系起来。

El estudio investiga los factores medioambientales que influyen en el deterioro de los murales de Ajanta, evaluando los efectos globales del turismo y el comportamiento de los visitantes sobre el entorno de las cuevas y los murales. Se examina el papel que desempeñan las partes interesadas en la conservación y aporta datos para orientar a instituciones como la encuesta arqueológica de India. El objetivo son las prácticas turísticas sostenibles para equilibrar el acceso del público con la conservación de los murales para las generaciones futuras.

A lo largo de 25 años, los visitantes de las cuevas de Ajanta se han duplicado, con un impacto en las condiciones microclimáticas de los murales antiguos. El estudio evalúa el impacto de los visitantes para establecer una regulación y determinar la capacidad de carga, teniendo en cuenta la temperatura, la humedad y la contaminación. La cuantificación de la capacidad plantea problemas debido a las variables. La investigación aporta información al turismo mundial y a la conservación del patrimonio, ofreciendo metodologías aplicables a sitios culturales de todo el mundo.

Este estudio examina los impactos ambientales en la conservación de los murales de las cuevas de Ajanta, incluyendo la humedad, el crecimiento microbiano, la exposición a la luz solar, la calidad del aire y la presencia de turistas. La afluencia de turistas aumenta los niveles de CO2, poniendo directamente en peligro los murales. La preocupación por las partículas, especialmente durante las visitas, pone de relieve la necesidad de tomar decisiones basadas en datos y de utilizar tecnología actual para proteger las obras de arte de las cuevas de Ajanta, algo crucial debido a su importancia mundial y a las vulnerabilidades relacionadas con el turismo.

Este estudio conlleva importantes implicaciones sociales con una resonancia global. La participación activa de la comunidad local y las partes interesadas del turismo en la conservación y promoción de las Cuevas de Ajanta fomenta el empoderamiento, generando un sentido de orgullo, propiedad y responsabilidad entre los residentes y garantiza el disfrute sostenible del patrimonio cultural al mismo tiempo que lo salvaguarda para las generaciones futuras. Además, habrá beneficios socioeconómicos para los residentes locales, como oportunidades de empleo como operadores turísticos, guías turísticos, personal de hostelería, artesanos, comerciantes de souvenirs, etc.

Este estudio integra la conservación del arte, las ciencias medioambientales, la preservación del patrimonio cultural y los aspectos sociales para abordar los retos del turismo mundial. Centrándose en un sitio de importancia mundial, ofrece estrategias prácticas para la conservación de las obras de arte, la gestión del turismo y los problemas medioambientales. Estas recomendaciones aportan soluciones reales aplicables a lugares patrimoniales de todo el mundo, tendiendo puentes entre el análisis científico y las percepciones sociales y culturales.