Dynamic Environment Research Articles

Exploring the dynamics of firm growth: the interplay of decision-making logic

Purpose This paper investigates how various emergent theoretical perspectives in entrepreneurship research, representing diverse decision-making logics, influence firm growth and evolution. It explores the interaction among decision-making logics, including experimentation, affordable loss, flexibility and pre-commitment as components of effectuation, alongside causation and bricolage and their synergistic effects on firm growth. Design/methodology/approach This study uses a multi-phase, discovery-oriented approach. Initially, insights from existing literature on decision-making logic were combined with in-depth interviews with 10 Iranian entrepreneurs within the food sector. This phase used alternative template research to evaluate the principles of effectuation, causation and bricolage within case study data depicting firm growth. Subsequently, a self-administered survey was developed based on these insights and distributed to 205 entrepreneurs in Iran. The survey data was analysed using fuzzy-set qualitative comparative analysis (fsQCA) to identify key factors and pathways influencing firm growth. Findings Using a discovery-oriented approach, this study formulates a comprehensive framework detailing decision-making logics that influence firm growth. Through fsQCA, 12 distinct paths are identified, highlighting the complex interplay of causation, effectuation and bricolage in high-growth firms within the food sector. Research limitations/implications This study has limitations. FsQCA identifies only logically sufficient combinations, suggesting potential for exploring alternative pathways in future research. Given COVID-19’s impact on the food sector, examining decision-making logic across diverse contexts and industries is advisable. Additionally, exploring how bricolage, causation and effectuation affect outcomes like new product development and innovation is essential in a growth-focused context. It is also important to consider environmental and organizational factors influencing growth. Originality/value This paper pioneers the examination of emerging theoretical paradigms in entrepreneurship and their impact on firm growth. It identifies critical configurations of causation, effectuation and bricolage, providing actionable insights for navigating dynamic business environments.

Rhythm and shift frequency of strategic renewal in Indonesian firm performance

Purpose This study aims to explore how manufacturing firms in emerging economies can effectively adjust the rhythm and shift frequency between exploitation and exploration renewal. The authors also examine how these strategic adjustments can significantly boost firm performance, offering insights into the dynamic process of strategic renewal. Design/methodology/approach This study analyzes annual reports of 127 Indonesian manufacturing firms from 2014 to 2019, applying both linear and curvilinear regression models to examine the hypotheses. Data on exploration and exploitation renewal were meticulously gathered using computer-aided text analysis, using targeted keywords to identify strategic renewal efforts. Findings The study shows that a rather irregular balance rhythm between exploitation and exploration renewal surprisingly enhances firm performance. A curvilinear relationship emerges as performance peaks when the shift frequency of renewal occurs about three times. This relationship optimizes the strategic renewal processes, emphasizing that firms need to remain agile and adaptable in today’s dynamic market environment. Originality/value This study leverages organizational learning to assess how the paradoxical dimensions of exploration and exploitation renewal impact firm performance. By focusing on the temporal transition of these tensions, it provides insights into optimizing the rhythm and shift frequency of renewal, transitioning from a static to a dynamic accord.

A study of electricity sales offer strategies applicable to the participation of multi-energy generators in short- and medium-term markets

Due to the increasing proportion of renewable energy, a multi-layered and multi-timescale energy market has emerged in many countries such as China. In the meanwhile, power generation companies must develop more intelligent and dynamic offer strategies to adapt to today's intricate energy trading. Because of the difficulty in describing the dynamic trading environment caused by the uncertainty of renewable energy, previous studies have not fully explored the offer strategy especially in both short-term and medium-term electricity markets. In response to this challenge, this research introduces a novel biding strategy framework leveraging a Asynchronous Advantage Actor-Critic (A3C) algorithm, which can effectively address the decision making in dynamic and uncertain energy markets. The framework focuses on intra-monthly transaction clearing mechanisms with the aim of optimally enhancing earnings. The research formulates an offer model both for thermal and renewable power generation enterprises, which is applicable to medium-term monthly and intra-monthly trading. The study then validates this framework through three distinct analyses: the returns of various bid methods under standard scenarios, the offer strategies return of power generation companies with diverse cost profiles, and the impact of varying renewable energy proportions. The multi-angle simulations confirm that the model presented in this paper offers a scientific basis for the development of offer strategies for power generation companies and enable power generating firms to effectively adopt to the current power market.

A distributed task allocation method for heterogeneous UAVs in dynamic and communication-constrained environments

A distributed task allocation method for heterogeneous UAVs in dynamic and communication-constrained environments

Spectral location for the universal scaling regime in Martian atmospheric turbulence

Atmospheric turbulence, irregular fluctuations of the fluid state, is studied on Mars. Universality of the turbulence spectrum underpins atmospheric models where computational requirements preclude full fidelity simulations of the smallest scales. However, there are discrepancies among reports on the existence and spectral location of universal scaling in Martian atmospheric data. Here, results indicate the smallest resolvable structures from Martian wind speed data are still associated with the energetic regime, which may ultimately explain why multiple reports have not found a consistent Kolmogorov-like spectral regime on Mars. Universal spectral scaling of wind data from Perseverance’s Mars Environmental Dynamics Analyzer is used to estimate the thresholds that separate three turbulence regimes: energetic, inertial, and molecular dissipation. Wind measurements at 2-Hz, the fastest sampling rate for direct wind sensor measurements on Mars, resolves turbulence in the energetic regime and approaches the inertial regime, which is consistent with reported Martian dust devil sizes.

PERAN PENYULUH BERBASIS MASYARAKAT DALAM MEMPERTAHANKAN HUTAN JANDA (KAOMBO OHUSII) DI KABUPATEN BUTON SULAWESI TENGGARA

This study aims to explore the management practices of Widow Forest (Kaombo Ohusii) in Buton Regency through the lens of local wisdom and development communication, by revealing the social, cultural, and environmental dynamics involved in forest management. This study uses a variety of data collection methods. Participant observation allows researchers to be directly involved in Widow Forest management activities, observe the practices carried out, and interact with local communities and other parties involved in natural resource management. In-depth interviews will be conducted with widows, poor women, community leaders, government officials, and non-governmental organizations involved in the management of Widow Forest. The results of the study show that community-based extension by traditional officials in Takimpo Village in the management of Widow Forest (Kaombo Ohusii) is an example of effective local participation in sustainable natural resource management. The involvement of this traditional institution also shows the importance of integrating local values in development, ensuring that decisions taken are in accordance with community expectations, and supporting environmental conservation and community empowerment.

Investigating the Impact of CSR Practices on Financial Performance: A Multiple Mediation Analysis in the Moroccan Exporting SMEs

Numerous pieces of research have shown that corporate social responsibility (CSR) orientation is key to driving long-term survival, growth and sustainable performance in a complex and dynamic environment. However, studies of the relationship between CSR and financial performance (FP) have yielded disparate results. Therefore, the current study aims to examine the effect of CSR on FP by using customer satisfaction (CS), reputation (REP) and competitive advantage (CA) as mediating variables. The data for this study was collected via a survey of 207 Moroccan exporting small and medium-sized enterprises (SMEs) and analyzed using SPSS 25 and Structural-equation Modeling with the aid of AMOS 23. Outcomes revealed that CSR has no significant direct effect on FP, but rather an indirect effect through REP and CS. Likewise, the link between CSR and FP is mediated on the one hand by CS and CA, successively and on the other hand by REP and CA, successively. These findings provide a verified theoretical framework for SME managers to plan and execute CSR to enhance FP by indirectly targeting the aforementioned intangible assets. The paper contributes to the knowledge of how CSR practices lead to FP in an under-explored context of SMEs in a developing country.

Direct and indirect selection in a proofreading polymerase.

The traits that affect evolvability are subject to indirect selection, as these traits affect the course of evolution over many generations rather than the direct replicative fitness of an individual. However, the evolution of evolvability-determining traits is often difficult to study because putative evolvability alleles often have confounding direct fitness effects of unknown origin and size. Here, we study theoretically and experimentally the evolution of mutation rates in proofreading polymerases with orthogonal control of direct and indirect selection. Mutagenic DNA polymerases enjoy a long-time fitness advantage by enhancing the rate of acquiring beneficial mutations. However, this is offset by a short-time fitness penalty, which we trace to a counterintuitive trade-off between mutation rates and activity in proofreading polymerases. Since these fitness effects act on different timescales, no one number characterizes the fitness of a mutator allele. We find unusual dynamic features in the resulting evolutionary dynamics, such as kinetic exclusion, selection by dynamic environments, and Rock-Paper-Scissors dynamics in the absence of ecology. Our work has implications for the evolution of mutation rates and more broadly, evolution in the context of an anti-correlation between mutation rates and short term fitness.

Stomatal development in the changing climate.

Stomata, microscopic pores flanked by symmetrical guard cells, are vital regulators of gas exchange that link plant processes with environmental dynamics. The formation of stomata involves the multi-step progression of a specialized cell lineage. Remarkably, this process is heavily influenced by environmental factors, allowing plants to adjust stomatal production to local conditions. With global warming set to alter our climate at an unprecedented pace, understanding how environmental factors impact stomatal development and plant fitness is becoming increasingly important. In this Review, we focus on the effects of carbon dioxide, high temperature and drought - three environmental factors tightly linked to global warming - on stomatal development. We summarize the stomatal response of a variety of plant species and highlight the existence of species-specific adaptations. Using the model plant Arabidopsis, we also provide an update on the molecular mechanisms involved in mediating the plasticity of stomatal development. Finally, we explore how knowledge on stomatal development is being applied to generate crop varieties with optimized stomatal traits that enhance their resilience against climate change and maintain agricultural productivity.

An adaptive hexagonal deployment model for resilient wireless sensor networks in precision agriculture

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol’s performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network’s robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol’s exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol’s prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications.

Acoustic Assaults on the Auschwitz-Birkenau Concourse

Abstract This article investigates how sonic encounters shaped Jews’ experiences on the concourse at Auschwitz-Birkenau. By applying methods associated with sound and sensory studies, the article examines survivors’ memoirs and interviews to demonstrate that sonic violence was a key aspect of the genocide. The train ramp was a dynamic acoustic environment in which interlocking noises swelled and contracted as part of the arrival operation. As Jewish arrivals deboarded the trains, an array of threatening noises battered them, including amplified commands blasted in German, antisemitic slurs, vicious barking, beatings, and gunshots. SS guards used these acoustic assaults to perpetrate sonic violence against the arrivals, subduing their targets by instilling fear through sound. By generating an atmosphere of chaos and confusion through sound, they achieved their chief objective: prisoner compliance. Acoustic assaults inflicted somatic and psychological pain that manifested itself physically. Many deportees responded to the sensorial onslaught with their own sonorous outpourings. Cries, wails, and screams flooded the concourse, evidence of their trauma. Besieged sensorially, many became mentally immobilized, underlining the capacity of sonic violence to impair cognition. Their mental paralysis amid the cacophonous maelstrom marked their shattered subjectivities, which seared themselves sonically into their memories.

Russia-China relationship in the context of the war in Ukraine: Bonding Over Shared Ideology or Western Sanctions

In the face of the Russia-Ukraine war, Russia’s trade and economic potentials have been limited and restricted mostly due to the imposition of sanctions. Consequently, a bond between Russia and China has developed, putting aside their former disagreements and disputes. This goes together with the fact that both countries face a shared adversary from the US and the West, which has worked in pulling them closer to each other. Amidst the dynamic environment of worldwide trade, the economic collaboration and strategic alignment between China and Russia stand out as a crucial axis. This article investigates the interdependency and asymmetries of their bilateral partnership and how they have shaped their global trade relation. Furthermore, it also analyzes the historical background of Sino-Russian connections, emphasizing significant milestones and policy changes that have influenced their present economic interconnection. It analyses the problem areas of their relation and whether this bonding will withstand future changes in the global order.

Assessing Digital Literacy Among Young Professionals: A Research Mapping Review in the Context of Knowledge Workers

This study examines the impact of digital transformation on knowledge workers, particularly young professionals, and their use of digital tools in the workplace. It addresses a gap in the literature by exploring how digital literacy affects adaptability, productivity, and job satisfaction in dynamic environments. Unlike previous studies that focus on educational settings or older knowledge workers, this research links digital literacy to employability, knowledge sharing, and innovation among young professionals. Using bibliometric analysis, the study identifies key themes such as digital literacy, knowledge management, and professional development. The findings show that digital literacy not only enables the use of new technologies but also fosters creativity and knowledge sharing, crucial in information-driven industries. The COVID-19 pandemic accelerated the need for digital adaptation, introducing challenges like technostress and information overload, which impact well-being and decision-making. This study underscores the importance of equipping young professionals with strong digital literacy to meet the demands of knowledge workers. It calls for organizations and educational institutions to prioritize digital skills training to bridge the digital divide and prepare the future workforce.

Temporal-enhanced Radar and Camera Fusion for Object Detection

Recently, object detection methods based on multimodal fusion have gained widespread adoption in autonomous driving, proving to be valuable for detecting objects in dynamic environments. Among them, millimetre wave (mmWave) radar is commonly utilized as an effective complement to cameras, as it is almost unaffected by harsh weather conditions. However, current approaches that fuse mmWave radar and camera often overlook the correlation between the two modalities, failing to fully exploit their complementary features. To address this, we propose a temporal-enhanced radar and camera fusion network to explore the correlation between these two modalities and learn a comprehensive representation for object detection. In our model, a temporal fusion model is introduced to fuse mmWave radar features from different moments, thus mitigating the problem of mmWave radar point-object mismatch due to object movement. Moreover, a new correlation-based fusion strategy using the dedicated mask cross attention is proposed to fuse mmWave radar and vision features more effectively. Finally, we design a gate feature pyramid network that selects shallow texture information based on deep semantic information to obtain more representative features. The experimental results on the nuScenes benchmark demonstrate the effectiveness of our proposed method.

Accelerated Nanocomposite Hydrogel Gelation Times Independent of Gold Nanoparticle Ligand Functionality.

The expansive use of hydrogels in healthcare relies on carefully tuned properties in dynamic environments with predictable behavior, including time sensitive biological systems and biomedical applications. To meet demands in these settings, nanomaterials are often introduced to a hydrogel matrix which simultaneously elevates potential applications while adding complexity to fundamental characteristics. With respect to drug delivery, gold nanoparticles have modifiable surfaces to carry an array of targeted drug treatments. However, different molecules acting as capping ligands possess different chemical structures that can impact gelation times. To understand the influence of capping ligand chemistry on polyacrylamide (PAM) based nanocomposite hydrogel radical gelation time, gold nanoparticle (Au NP) capping ligands were selected to encompass varying functional groups and molecular weights: citrate, cetyltrimethylammonium bromide, polyvinylpyrrolidone, and poly(acrylic acid). Gelation times were quantified as the storage-loss moduli crossover point in rheological time sweeps at constant strain and frequency. The dominating factor for gelation time was the presence of Au NPs, independent of a diverse range of capping ligand structures. The gelation times were also markedly faster than the same capping ligand structures used as stand-alone molecular additives. The accelerated Au NP gelation times, under 2 min, are attributed to the Au NPs acting as a cross-linker, promoting gelation. These results bolster the potential implementation of Au NP nanocomposite hydrogels in time-sensitive biomedical applications as robust drug carriers.

Estimating best nanomaterial for energy harvesting through reinforcement learning DQN coupled with fuzzy PROMETHEE under road-based conditions

Energy harvesters based on nanomaterials are getting more and more popular, but on their way to commercial availability, some crucial issues still need to be solved. The objective of the study is to select an appropriate nanomaterial. Using features of the Reinforcement Deep Q-Network (DQN) in conjunction with Fuzzy PROMETHEE, the proposed model, we present in this work a hybrid fuzzy approach to selecting appropriate materials for a vehicle-environmental-hazardous substance (EHS) combination that operates in roadways and under traffic conditions. The DQN is able to accumulate useful experience of operating in a dynamic traffic environment, accordingly selecting materials that deliver the highest energy output but at the same time bring consideration to factors such as durability, cost, and environmental impact. Fuzzy PROMETHEE allows the participation of human experts during the decision-making process, going beyond the quantitative data typically learned by DQN through the inclusion of qualitative preferences. Instead, this hybrid method unites the strength of individual approaches, as a result providing highly resistant and adjustable material selection to real EHS. The result of the study pointed out materials that can give high energy efficiency with reference to years of service, price, and environmental effects. The proposed model provides 95% accuracy with a computational efficiency of 300 s, and the application of hypothesis and practical testing on the chosen materials showed the high efficiency of the selected materials to harvest energy under fluctuating traffic conditions and proved the concept of a hybrid approach in True Vehicle Environmental High-risk Substance scenarios.

Analysis of Financial Stability in Indian Automobile Industry

The Indian automobile industry plays a pivotal role in the nation's economic landscape, contributing significantly to GDP, employment, and technological advancement. In recent years, the sector has faced considerable challenges, including shifting consumer preferences, regulatory changes, economic uncertainties, and the impact of global disruptions such as the COVID-19 pandemic. This paper aims to analyze the financial stability of the Indian automobile industry by evaluating key financial indicators, such as profitability, liquidity, and solvency. It also explores the industry's adaptability in the face of macroeconomic challenges, including supply chain disruptions, rising input costs, and changing regulatory landscapes related to environmental standards. Furthermore, the paper assesses the influence of government initiatives like the Production-Linked Incentive (PLI) scheme and electric vehicle adoption on the overall financial health of the sector. By employing both qualitative and quantitative analyses, the study provides insights into the sustainability and resilience of the Indian automobile industry, highlighting the critical factors that determine its financial stability and its potential for future growth in a dynamic economic environment.

Dynamic microbiome diversity shaping the adaptation of sponge holobionts in coastal waters.

During long-term evolution, sponge holobionts, among the oldest symbiotic relationships between microbes and metazoans, developed two distinct phenotypes with high- and low-microbial abundance (HMA and LMA). Despite sporadic studies indicating that the characteristic microbial assemblages present in HMA and LMA sponges, the adaptation strategies of symbionts responding to environments are still unclear. This deficiency limits our understanding of the selection of symbionts and the ecological functions during the evolutionary history and the adaptative assessment of HMA and LMA sponges in variable environments. Here, we explored symbiotic communities with two distinct phenotypes in a 1-year dynamic environment and combined with the meta-analysis of 13 sponges. The different strategies of symbionts in adapting to the environment were basically drawn: microbes with LMA were more acclimated to environmental changes, forming relatively loose-connected communities, while HMA developed relatively tight-connected and more similar communities beyond the divergence of species and geographical location.

Unpacking the Impact of Asset Structure and Sales Growth on Capital Structure: The Moderating Influence of Profitability

This study explores the impact of asset structure and sales growth on capital structure, emphasizing the moderating influence of profitability. As firms navigate the complexities of financing decisions, understanding how these variables interact is crucial for optimizing capital structure. The findings reveal that asset structure and sales growth significantly affect capital structure, with profitability playing a critical role in moderating these relationships. Firms with substantial tangible assets are better positioned to leverage debt financing, while those demonstrating strong sales growth are viewed favorably by investors and creditors. However, the extent to which sales growth influences capital structure is contingent upon profitability; high profitability enables firms to capitalize on growth opportunities, whereas low profitability may inhibit their capacity to leverage growth potential. Empirical research supports these conclusions, indicating that asset structure, sales growth, and profitability significantly shape capital structure decisions across various industries. Ultimately, this study provides valuable insights for financial managers, highlighting the importance of balancing growth aspirations with profitability to achieve effective capital structure management. This, in turn, can lead to sustained competitive advantage, a state where a firm outperforms its competitors over a prolonged period in a dynamic economic environment.

A multi-objective reinforcement learning framework for real-time drilling optimization based on symbolic regression and perception

In the context of the global energy transition, optimizing deep-water oil and gas drilling parameters is crucial for ensuring safety while improving efficiency. Traditional methods face limitations in highly dynamic and nonlinear drilling environments, struggling to balance speed and cost-effectiveness. Furthermore, these methods rely on real-time logging-while-drilling (LWD) data for decision-making, but delays in data collection and processing hinder timely adjustments of drilling parameters, affecting decision accuracy and responsiveness. This paper proposes a multi-objective drilling parameter optimization framework, incorporating symbolic regression, time-series networks, and Markov decision processes to precisely predict ROP, formation conditions, and optimize drilling parameters in real time. Key innovations include a multi-population evolutionary symbolic regression algorithm for constructing empirical equations, the integration of variational mode decomposition (VMD) and sample entropy for data preprocessing, and multi-head self-attention time-series networks to enhance prediction accuracy. Quantile regression further estimates the range of drilling parameter adjustments. Additionally, a drilling parameter optimization deep deterministic policy gradient (DPODDPG) algorithm was developed to automate real-time parameter adjustments. Empirical analysis on the Ledong 10-1 block in the South China Sea demonstrated significant improvements: ROP increased from 54.18 m/hr to 122.17 m/hr, mechanical specific energy (MSE) decreased from 100.82 MPa to 97.78 MPa, and cost per foot reduced from 121.16 × 102 CNY/m to 51.31 × 102 CNY/m. Compared to traditional methods, the proposed framework showed clear advantages in enhancing ROP, reducing MSE, and controlling costs, further validating its superiority in complex drilling environments. This method not only significantly improves drilling efficiency and economic benefits but also adapts to complex and changing drilling conditions, showing broad application potential, particularly in challenging deep-water oil and gas drilling operations, where it can provide more efficient and reliable optimization solutions.