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Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

Based on the reinforcement sensitivity theory (RST), this study aims to examine the key factors that influence users’ renewable energy (RE) continuance intention. More importantly, the mediating role of excitement and the moderating role of technology infrastructure (TEC) and anxiety in the renewable energy continuance intention (RECI) is explored.

A survey was conducted among RE users in Malaysia and the results of a questionnaire survey of 397 were analysed using partial least square structural equation modelling.

The study’s findings highlight the significant roles of excitement and anxiety in RE intentions, emphasizing the impact of psychological factors and government policy on RE adoption in Malaysia. It also proves the moderating effect of perceptual dimension attributed to TEC.

This study significantly advances the understanding of RECI, offering a replicable research framework that can be examined across regions and countries. Scholars can leverage the framework for further exploration, whereas managers should recognize reinforcement sensitivity influences on RECI and the impact of perceived technology infrastructural support.

Given that this study is a pioneer attempt to investigate the approach and inhibiting factors relating to RECI through the application of RST, It provides novel insight for future research on RE among researchers and practitioners, thereby contributing to the limited body of knowledge on the psychological dynamics of RECI of an emerging economy.

This research aims to investigate the interplay between flexibilities and strategic orientations in the context of supply chain agility (SCA), particularly in the medical equipment supply chain. The study seeks to identify key internal and external flexibility factors, along with the firm’s strategic orientations, and understand why and how these factors are interrelated and contribute to the development or enhancement of SCA.

The study adopts an inductive exploratory multiple case study design to empirically identify and examine the underlying flexibility and strategic orientation factors and their link to SCA. Data collection tools included semi-structured interviews and access to key company documentation and archives from six major medical equipment suppliers in Jordan.

The research findings lead to the proposal of an emerging theoretical model describing the nature of relationships among internal/external flexibility factors, strategic orientations and SCA, with underlying research propositions that can later be subjected to deductive testing and empirical quantitative validation.

This research advances the theoretical understanding of SCA by investigating its strategic antecedents, including various orientations and their direct and indirect effects. Second, it provides a comprehensive insight into the combined impact of internal and external flexibilities on SCA, an aspect relatively underexplored in previous literature.

A highly selective cyanide phenoxazine-based fluorescence chemosensor POH was created to detect cyanide (CN) ions.

A malonitrile was added to a phenoxazine fluorophore to make this widely available chemosensor. By fluorescence spectroscopy, the sensor POH showed turn-off fluorescence emission for CN with 2:1 binding stoichiometry in CH₃CN/H₂O (90:10 v/v) medium.

The detection limits for CN were 9.8 × 10⁻⁹ M, which were much lower than WHO standards. NMR and FT-IR investigations backed up the suggested sensor POH mechanism.

The detection CN method should be applicable in a number of situations, where the CN anion for fresh water and drinking water has to be quickly and accurately analyzed.

The purpose of this study is to provide a comprehensive overview of sustainable fashion consumption from a consumer behavior perspective, combining scientometric analysis and the SPAR-4-SLR protocol to identify trends, key contributors and research gaps in the field.

The study analyzes 114 articles published between 2014 and 2024, sourced from the Scopus database. A hybrid approach is used, employing VOSviewer and Rstudio for quantitative analysis, along with the theory-context-characteristics-methodology framework to systematically review constructs, theories, contexts and methodologies in the selected articles.

The findings highlight critical insights into consumer behavior regarding sustainable fashion and identify gaps in the literature. The study also provides performance indicators, including publication trends and citation metrics, visualized through tables and maps. It offers practical guidance for businesses and policymakers to promote sustainable consumption practices.

This research contributes to the field by integrating scientometric and systematic review methods, providing a novel approach to understanding sustainable fashion consumption. It also suggests future research directions and explores how benchmarking techniques can enhance consumer engagement and sustainability strategies.

Biofortification is a promising strategy for addressing malnutrition and food insecurity by enriching staple crops with nutrients. However, farmers’ adoption is crucial for their success. This study aims to shed light on Ethiopian farmers' intentions to adopt biofortified orange-fleshed sweet potatoes (OFSP) using the motivation, opportunity and ability (MOA) framework.

This study used data from a cross-sectional survey of 370 sweet potato farmers in Ethiopia. Binary logistic regression analysis was employed to examine the factors influencing farmers' intention to adopt OFSP.

Ability-, motivation- and opportunity-related factors significantly influenced adoption intention. Specifically, knowledge of OFSP benefits, production skills, crop characteristics and access to institutional services positively influences adoption. However, farming experience has a negative effect.

These findings emphasize the importance of educating farmers about OFSP’s high vitamin A content and favourable agronomic features. Additionally, understanding farmers' experiences and enhancing their production skills are crucial for increasing adoption rates. Targeted educational programs and support services can address these gaps and deficiencies.

This study is the first to analyse Ethiopian farmers' adoption of OFSP using an MOA framework. It offers a novel approach to understanding the complex interplay of psychological factors that influence adoption intentions. Furthermore, this study provides valuable insights into the Ethiopian context, filling a gap in the literature. These findings contribute to the development of targeted strategies for promoting biofortified crops in Ethiopia and other similar global contexts.

The purpose of this study is to examine the role of venture capital (VC) in supporting corporate growth and innovation through participation in private placements. While VC provides essential financial support to companies, it remains unclear whether this involvement serves a strategic investment role or a purely financial one. This study seeks to elucidate the role of VC by analyzing changes in the price discount of private placements following VC participation.

The authors take the private placement events of China A share listed companies from April 2005 to January 2023 as the sample, and examine the influence of VC subscriptions on price discount rate.

VC subscriptions to private placements increase information asymmetry, consequently raising the discount rate. This relationship is influenced by the transaction characteristics and information environment. Specifically, VC subscriptions further elevate the discount rate when VC are geographically dispersed from the issuers, possess industry expertise in the issuers’ sector, allocate raised funds for asset restructuring or non-digital investments and when the issuers are in their growth stages. Moreover, the positive correlation between VC subscriptions and the discount rate is more pronounced under conditions of lower internal control quality and weaker external media supervision. Higher discount rates in VC-subscribed private placements result in lower R&D investment and investment efficiency by the issuers, leading to larger-scale VC sell-offs and ultimately diminishing the market and financial performance of the issuers.

The issuers should diligently assess the behaviors and motives of VC and selectively choose issuance targets and methods to manage risks associated with price deviations in private placements. Additionally, this study recommends that regulatory authorities develop a more detailed regulatory framework that considers transaction characteristics and the information environment. This strategy should help optimize external regulatory measures like media coverage and protect the interests of small and medium-sized investors.

This study extends research on the “name chasing” motive and certification effect of VC in private placements, enriches the literature on the mechanisms forming discount rates and provides insights for refining regulatory policies on private placements.

The purpose of this study is to analyze the seasonal spatiotemporal climate variability in the Borena zone of Ethiopia and its effects on agriculture and livestock production. By examining these climate variables in relation to global sea surface temperatures (SST) and atmospheric pressure systems, the study seeks to understand the underlying mechanisms driving local climate variability. Furthermore, it assesses how these climate variations impact crop yields, particularly wheat and livestock production, providing valuable insights for developing effective adaptation strategies and policies to enhance food security and economic stability in the region.

The design and methodology of this study involve a multifaceted approach to analyzing seasonal spatiotemporal climate variability in the Borena zone of Ethiopia. The research uses advanced statistical techniques, including rotated empirical orthogonal function (EOF) and rotated principal component analysis (RPCA), to identify and quantify significant patterns in seasonal rainfall, temperature and drought indices over the period from 1981 to 2022. These methods are used to reveal the spatiotemporal variations and trends in climate variables. To understand the causal mechanisms behind these variations, the study correlates seasonal rainfall data with global SST and examines atmospheric pressure systems and wind vectors. In addition, the impact of climate variability on agricultural and livestock production is assessed by linking observed climate patterns with changes in crop yields, particularly wheat and livestock productivity. This comprehensive approach integrates statistical analysis with environmental and agricultural data to provide a detailed understanding of climate dynamics and their practical implications.

The findings of this study reveal significant seasonal spatiotemporal climate variability in the Borena zone of Ethiopia, characterized by notable patterns and trends in rainfall, temperature and drought indices from 1981 to 2022. The analysis identified that over 84% of the annual rainfall occurs during the March to May (MAM) and September to November (SON) seasons, with MAM contributing approximately 53% and SON over 31%, highlighting these as the primary rainfall periods. Significant spatiotemporal variations were observed, with northwestern (35.4%), southern (34.9%) and northeastern (19.3%) are dominant variability parts of the zone during MAM season, similarly southeastern (48.7%), and northcentral (37.8%) are dominant variability parts of the zone during SON season. Trends indicating that certain subregions experience more pronounced changes in climate variables in both seasons. Correlation with global SST and an examination of atmospheric pressure systems elucidated the mechanisms driving these variations, with significant correlation with the southern and central part of Indian Ocean. This study also found that fluctuations in climate variables significantly impact crop production, particularly wheat and livestock productivity in the region, underscoring the need for adaptive strategies to mitigate adverse effects on agriculture and food security.

The implications of this study highlight the need for robust adaptation strategies to mitigate the effects of climate variability. Detailed research on seasonal climate patterns and the specific behaviors of livestock and crops is essential. Gaining a thorough understanding of these dynamics is critical for developing resilient adaptation strategies tailored to the unique ecological and economic context of the Borana zone. Future research should focus on seasonal climate variations and their implications to guide sustainable development and livelihood adjustments in the region.

This study offers significant originality and value by providing a detailed analysis of seasonal spatiotemporal climate variability in the Borena zone of Ethiopia, using advanced statistical techniques such as rotated EOF and RPCA. By integrating these methods with global SST data and atmospheric pressure systems, the research delivers a nuanced understanding of how global climatic factors influence local weather patterns. The study’s novel approach not only identifies key trends and patterns in climate variables over an extensive historical period but also links these findings to practical outcomes in crop and livestock production. This connection is crucial for developing targeted adaptation strategies and policies, thereby offering actionable insights for enhancing agricultural practices and food security in the region. The originality of this work lies in its comprehensive analysis and practical relevance, making it a valuable contribution to both climate science and regional agricultural planning.

This paper examines issues that focus on the importance of accounting data generated knowledge information and its role in modern business. The cognitive aspect of this research reflects the ability of companies and its employees to apply knowledge for managerial purposes using accounting data.

Using a questionnaire, a five-factor model related to information communication, information cognitive utilization, functional optimization, applicability and cognitive efficiency was created.

Findings present a series of complex correlations highlighting possible actions to utilize knowledge as a tool for management. Information is obtained regarding the management of knowledge and the adoption of information systems.

The results reflect the limited implementation of intellectual capital practices and understanding of knowledge as a financial tool for executives and employees. Based on the above, an attempt was made to formulate the questions for the careful identification of the factors.

Rapid developments in information and communication technologies, together with a realization that knowledge is a resource of general and cost strategic importance, changed the operational structures of companies, shifting value from materials to intangible assets. This paper demonstrates how multiple variables are correlated and how small changes could help increase intellectual capital and facilitate the construction of knowledge based systems.

The need for an accounting valuation of intellectual capital in order to present the true picture of business value is evident. This paper illustrates factors such as interactive communication and systematic cognitive efficiency or the monetization of information as a preliminary step for future valuation and management intellectual capital models.

Direct access to sufficient and reliable information, lead to the search for effective tools for the creation, aggregation and exchange of knowledge. The latter becomes a key goal for information systems. Emphasis is placed on the benefits and critical success factors of knowledge management systems, as essential information systems to support and enhance organizational processes.

To evaluate the efficacy of a PRECEDE model-based educational program in enhancing awareness, attitudes and preventive behaviors against diabetes complications among rural patients with type 2 diabetes mellitus (T2DM).

This quasi-experimental study included 160 T2DM patients from rural health centers in Andimeshk, Iran (2020–2021). Participants were randomly assigned to intervention (n = 80) or control (n = 80) groups. The intervention group received a six-session in-person educational program, supplemented by weekly lifestyle videos via WhatsApp over six weeks, guided by PRECEDE model constructs. A validated questionnaire assessed knowledge, attitudes, enabling and reinforcing factors, and preventive behaviors pre- and post-intervention.

Post-intervention, the intervention group demonstrated significantly (p < 0.001) higher mean scores in knowledge, attitudes, enabling factors, reinforcing factors and preventive behaviors than controls. Fasting blood sugar (FBS) levels were significantly (p < 0.001) reduced in the intervention group at three-month follow-up.

The findings demonstrate that a comprehensive program grounded in the PRECEDE model can significantly enhance patients’ awareness, strengthen positive attitudes, increase enabling and reinforcing factors and promote preventive lifestyle behaviors. Importantly, the intervention led to improved glycemic control, as evidenced by reduced fasting blood sugar levels at three-month follow-up. These results suggest that tailored, theory-driven educational initiatives have the potential to empower rural patients with type 2 diabetes to make sustainable lifestyle changes and better manage their condition. The study provides a model for developing and implementing effective diabetes self-management programs in underserved rural communities, which can ultimately contribute to reducing diabetes-related complications and improving overall health outcomes in this population.