Complex Phenomenon Research Articles

The Evaluating Land Regulation Challenges in Mitigating Gentrification: Insights from Bali's Mass Tourism Impact

Gentrification is a complex socio-economic phenomenon that often occurs in tourism areas, where areas previously inhabited by lower-class residents are transformed into more prosperous neighborhoods. In Bali, significant tourism growth, with more than 5.5 million foreign tourists, has driven demand for land and property, especially from wealthy investors and foreigners. This has led to a surge in property prices that are unaffordable for locals, who on average have an income of around Rp 3.5 million per month. Drastic increases in land prices, especially in regions such as Canggu and Ubud, have resulted in the marginalization of local communities and the eviction of indigenous peoples, while also highlighting the growing gap between the rich and the poor. This study aims to explore the relationship between inadequate land regulation and the gentrification phenomenon triggered by mass tourism in Bali. Using socio-legal methods, this study analyzes primary and secondary data, including national regulations and legal literature. The results show that gaps in land regulation, such as the use of nominee agreements and lack of oversight of land ownership by foreign investors, exacerbate gentrification. Therefore, it is important to improve existing regulations by emphasizing the protection of the rights of local communities and the development of inclusive land-use policies. This study recommends the involvement of local communities in every stage of tourism development, as well as the enforcement of laws that limit foreign dominance in property ownership. As such, the principle of social justice should be the basis of tourism planning in Bali, ensuring that economic benefits are evenly distributed and supporting the cultural sustainability of local communities.

Flow Characteristics and Pressure Pulsation Analysis of Cavitation Induced in a Double-Volute Centrifugal Pump

Cavitation is a complex multiphase flow phenomenon, and the generation of transient phase transitions between liquid and vapor during cavitation development leads to multi-scale vortex motion. The transient cavitation dynamics and centrifugal pump’s rotor–stator interaction will induce pressure fluctuations in the impeller and the volute fluid of the centrifugal pump, resulting in a complex flow field structure. Based on the Schnerr–Sauer cavitation model and SST k-ω turbulence model, this paper studies the transient characteristics of the cavitation-induced unsteady flow in the centrifugal pump and the excitation response to the pressure pulsation in the volute under different flow conditions, taking the large vertical double-volute centrifugal pump as the research object. The results indicate the following: As the impeller rotates, in the external excitation response, the jet-wake flow structure at the centrifugal pump blade outlet shows an increase in the blade frequency signal. This is evident near the measurement points of the volute tongue and separator. When severe cavitation occurs, the maximum amplitude at the blade frequency in the volute shifts from the pump tongue (30°) to the downstream of the tongue (45°). The value of fpmax is 3.1 times that when NPSHa = 8.88 m. By applying the Omega vortex identification method, it can be seen that the interaction between the vortices at the blade trailing edge and the stable vortex in the volute tongue undergoes a process of elongation, fusion, separation, and recovery. This represents the downstream influence of the impeller on the volute. When Q = 0.9Qd, the process of the blade passage vortex tail detaching and dissipating in the impeller flow path can be observed, demonstrating the upstream influence of the volute on the impeller.

Human-centered GeoAI foundation models: where GeoAI meets human dynamics

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

Mosquito-repellent textiles: a systematic review of material innovations, application principles and research trends

In many parts of the world, mosquito-borne illnesses have become a serious threat to public health. The development of mosquito-repellent textiles (MRTs) presents a promising approach to mitigating the risk of mosquito bites and the transmission of vector-borne illnesses. Despite the tremendous advancements in research over the years, it is uncommon to find a systematic review that maps the global research landscape with an in-depth analysis of MRT literature. Using the PRISMA approach, a total of 127 articles were carefully selected in March 2024 from Scopus and Google Scholar databases spanning 1989 to 2024. The data were carefully screened, extracted, and analyzed to understand the geospatial research collaborations, technological and materials innovations, application principles, and the current research trends in MRTs. Current innovations point to the fabrication of sustainable mosquito-repellent inherent polymers, and the use of numerical simulations to understand the complex phenomena of mosquito interactions with humans and textile substrates through machine learning. The worldwide research on MRT reveals an increasing trend toward the design and fabrication of sustainable MRTs using bio-based materials with significant efforts in the design of polymers with inherent mosquito-repellence properties. These findings will help researchers, policymakers, and stakeholders make informed choices about future research directions, funding priorities, and partnerships for the fight against mosquito-borne diseases globally.

Burnout and Sources of Stress among Primary and Secondary Teachers in the Republic of North Macedonia

The study focuses on professional burnout and its sources of workplace stress among teachers. Burnout, as a complex psychological phenomenon, is characterized by emotional exhaustion, depersonalization and reduced professional efficiency. Teachers play a crucial role in the development of our future generations and their well-being is central to their ability to effectively fulfil this role. The general aim of the research is to measure burnout levels and sources of stress among primary and secondary teachers who are working in public schools in the Republic of North Macedonia, by examining the degree of emotional exhaustion, depersonalization and personal accomplishment. The research sample is randomly chosen and consists of 320 teachers. To collect data, a personal information form and Maslach Burnout Inventory were used to measure burnout levels of teachers. The obtained results found that primary and secondary teachers in public school in Republic of North Macedonia experienced a high level of emotional exhaustion and an average level of depersonalization and personal accomplishment dimension. Emotional exhaustion represents a critical warning sign of burnout, signaling the need for individual and organizational interventions to restore balance and resilience. The research detects nine significant factors that cause stress and professional burnout among teachers. The present study has contributed to the field by providing new insights into the stress and burnout experienced by primary and secondary teachers and should encourage education policies makers, national and local education authorities in developing a healthy, safe and productive environment for the growth and development of all involved in. Instead of looking at teacher burnout as an individual problem, leaders in education must shift their focus to assess the problem as a systemic, institutional, or policy-based issue.

Sea Surface Temperature Forecasting Using Foundational Models: A Novel Approach Assessed in the Caribbean Sea

Sea surface temperature (SST) plays a pivotal role in air–sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. This study evaluates the performance of foundational models, Chronos and Lag-Llama, in forecasting SST using 22 years (2002–2023) of high-resolution satellite-derived and in situ data. The Chronos model, leveraging zero-shot learning and tokenization methods, consistently outperformed Lag-Llama across all forecast horizons, demonstrating lower errors and greater stability, especially in regions of moderate SST variability. The Chronos model’s ability to forecast extreme upwelling events is assessed, and a description of such events is presented for two regions in the southern Caribbean upwelling system. The Chronos forecast resembles SST variability in upwelling regions for forecast horizons of up to 7 days, providing reliable short-term predictions. Beyond this, the model exhibits increased bias and error, particularly in regions with strong SST gradients and high variability associated with coastal upwelling processes. The findings highlight the advantages of foundational models, including reduced computational demands and adaptability across diverse tasks, while also underscoring their limitations in regions with complex physical oceanographic phenomena. This study establishes a benchmark for SST forecasting using foundational models and emphasizes the need for hybrid approaches integrating physical principles to improve accuracy in dynamic and ecologically critical regions.

Sensitivity to Cavitation and Turbulence Parameters in Asymmetric Nozzles Flow Numerical Simulations

Cavitating flows are a complex phenomenon involving turbulent flow and phase change, both of which must be considered in its modeling. This study examines the sensitivity of the simulated flow in asymmetric nozzles to the cavitation and turbulence models calibration parameters involved. Building upon previous studies and using the Computational Fluid Dynamics tool Open-FOAM(R), a more detailed investigation was performed based on the k−omega SST turbulence model and the Schnerr-Sauer cavitation models for cases of quite developed cavitation. The results reinforce previous conclusions related to the suitable cavitation and turbulence models calibration on the representation of the cavitated zone.

AI-led study of dynamic changes in milk containing hybrid nanoparticles in an electromagnetically vibrated channel subjected to thermal oscillations and rapid pressure changes: Implications for dairy industry

Oscillating electromagnetic forces generated from a vibrated Riga plate have broad implications across various scientific and engineering domains. Artificial intelligence (AI) is applied to optimize precision and energy efficiency in pasteurization and sterilization by regulating the thermal and dynamic behavior of nanoparticle-infused milk under electromagnetic heating. The technique ensures accurate temperature control, minimizes the risk of overheating, and preserves the milk's nutritional and sensory qualities. It focuses on predicting the thermal and dynamic behaviors of milk infused with silver and zinc oxide nanoparticles in an electromagnetically vibrated channel experiencing thermal oscillations and rapid pressure changes. The research integrates complex physical phenomena such as radiant heat emission and Darcy drag forces, employing Darcy's model to delve into drag within porous media. Detailed mathematical and physical descriptions of milk flow dynamics are established, with solutions efficiently derived using the Laplace Transform (LT) method. The results, encompassing shear stress (SS) and rate of heat transfer (RHT) analyses, are detailed in tables and graphs. Findings indicate enhanced milk momentum with higher modified Hartmann number and reduced momentum with wider electrode spacing. Elevated oscillation frequencies of the left channel wall stabilize milk flow in both hybrid nano-milk (HNM) and nano-milk (NM). Larger Casson parameter improves SS, while higher radiation parameter reduces RHT. An AI-driven artificial neural network (ANN) is employed for precise estimations, achieving 98.022% accuracy in SS testing, 98.99% in cross-validation, and a flawless 100% accuracy for RHT. The research findings can be implemented to precisely control the mixing of milk and its physical features at a molecular level, enabling more even heat distribution and faster, more efficient pasteurization or homogenization processes.

The dominating dimensionless numbers of adiabatic shear localization

Adiabatic shear is a complex phenomenon involving thermo-mechanical coupled failure mechanisms, which is affected by material properties, loads, and geometries. In this study, four dimensionless numbers which only contain input parameters and can fully reflect the influence of adiabatic shear are determined by reducing the conservation equations of shear localization to dimensionless terms. The dimensional analysis method of adiabatic shear, along with predictive models for the characteristic parameters of adiabatic shear, are systematically provided by revealing the physical significance of the dimensionless numbers. Based on data analysis, a dimensional analysis method of adiabatic shear for multi-physical processes is proposed, which has been successfully applied to explosively-driven metal shells, to realize the prediction and control of adiabatic shear. This study demonstrates that the prediction models of adiabatic shear-band spacing and width can be unified through a relationship involving the Prandtl number, Pr. Furthermore, the classical prediction models of spacing and width are improved based on the experimental data. It is clearly pointed out that the more favorable the formation of shear localization, the smaller the width and spacing of the shear band, which illustrates the influence of material properties and loads on the spatial distribution of shear bands. In addition, a new prediction model for the propagation velocity of the shear band including Pr is proposed by using dimensional analysis. Compared with the classical model, the new model has higher accuracy, and can correctly reflect the influence of loads, material mechanical and thermophysical properties on the shear-band velocity.

“What I do not see and others see in me”: mutual aid for suicide recovery in Colombia

ABSTRACT Purpose In response to the rise in suicidal behaviour and the strategies to address it, community actions represent a comprehensive approach to the factors influencing this situation. The aim of this study was to analyse the influence of mutual support on the recovery of individuals who experienced suicidal behaviour and attended community mental health groups. Methods A qualitative study was conducted in 2023. Focus groups were used as a data collection technique, followed by thematic analysis. Participants were selected based on convenience from mutual support groups registered with the Ministry of Health of Colombia. Results Two main themes were identified in the thematic analysis. The first theme, “Own Reasons Through Others,” was composed of categories such as “What Others See in Me” and “The Importance of Being in the Same Boat.” The second theme was titled “The Community as a Space for Suicide Prevention,” with categories including “Human Rights for Recovery” and “What We Can Achieve Together.” Conclusions Pluralism, understood as the integration of diverse perspectives, is crucial for addressing complex phenomena such as mental health and community interventions. The study demonstrates how mutual aid groups and their members can generate collective and policy actions effective for managing suicidal behaviour.

Double Burden of Malnutrition: A Narrative Review of its Drivers and Mitigation Strategies in Urban Settings

Background: Double burden of malnutrition (DBM) is an emerging public health problem in urban settings where both undernutrition and obesity exist. This complex phenomenon is caused by socioeconomic disparities, urbanization, and dietary transitions, with women and children being proportionately affected. This study reviews the drivers of DBM and evaluates suitable mitigation strategies. Methods: A comprehensive review was performed using studies published between 2014 and 2024. Peer-reviewed articles examining the drivers, interventions, and outcomes of DBM within urban contexts were the target inclusion criteria. Data were extracted on location, target populations, methodologies, key drivers, interventions and outcomes which were then synthesized using thematic analysis. Results: Eighteen studies from Sub-Saharan Africa, Southeast Asia, and Latin America were included. Drivers known to be common among the reviewed studies include income inequality, poor dietary diversity, and cultural food practices that are further worsened by urbanization and sedentary lifestyles. By region, effective mitigation strategies included maternal nutrition literacy programs in Southeast Asia and Sub-Saharan Africa and taxation policies against unhealthy foods in Latin America. Gaps were identified in the areas of policy implementation and program scalability. Conclusion: Integrated, double-duty actions are needed to address the double burden of malnutrition in urban contexts. However, policy reform, community-based nutritional programs and educational initiatives targeted at vulnerable populations, are essential. Longitudinal and gender-specific research should be prioritized in assessing DBM’s trajectory and informing future sustainable interventions. The findings from this study offer actionable insights for policymakers and public health practitioners working to eliminate DBM in urban settings.

How do age and sex influence pain threshold and tolerance among Santal tribal people living in West Bengal, India?

The perception of pain, encompassing pain threshold and tolerance levels, is a complex phenomenon influenced by biological, psychological, and sociocultural factors. Notably, age and sex have consistently emerged as pivotal determinants in modulating pain perception. The study aimed to examine age and sex differences in pain threshold and tolerance levels. Furthermore, it delved into exploring whether age-related differences in pain threshold and tolerance levels vary between males and females. This study incorporated 484 healthy Santal tribal individuals aged 18–88 years (male 203 and female 281) living in Howrah and Purba Bardhaman Districts of West Bengal State, India, who reported no chronic or significant pain at the time of data collection. Pain threshold and tolerance levels were assessed using a digital algometer. Results of two-way ANOVA revealed significant main effects of age and sex on every pain threshold and tolerance level assessed in this study, indicating that older individuals had lower pain threshold and tolerance levels than younger ones. Males demonstrated greater levels of pain threshold and tolerance relative to females. Age and sex showed a significant interaction effect on pain tolerance levels, but not on pain threshold levels demonstrating the age-associated declining trend in pain threshold levels was consistent for either sex; however, such a tendency in pain tolerance levels was more pronounced in men. These findings highlight the importance of considering age and sex factors when assessing pain perception.

Experiences of self-gifting luxury fashion during the COVID-19 pandemic

Consumer behaviour is known to change during trying times as consumption has implications for the self. In fact, the beginning of the COVID-19 pandemic saw a rise in self-gifting. Adopting a psychological lens, this novel study explored females’ experiences of self-gifting luxury fashion during the pandemic. Given the under-researched area, an exploratory qualitative approach was adopted and interpretative phenomenological analysis was used to understand participants’ experiences. A homogenous sample of four females were interviewed using semi-structured interviews to elicit rich data. Findings demonstrated that self-gifting of luxury fashion during the pandemic was a complex phenomenon that seemed to contribute to participants’ psychological well-being. The discussion links and extends the literature on self-gifting, luxury consumer behaviour and consumption during trying times. It highlights implications for academic research, individuals, brands and retailers.

Drought Assessment in Kalaburgi District, North-eastern Dry Zone of Karnataka, India by Using Remote Sensing and Google Earth Engine

Drought is a significant natural disaster exacerbated by global warming, leading to severe environmental, economic and societal impacts. As one of the most complex phenomena, drought requires advanced methods for effective monitoring and assessment. Remote sensing indices have proven effective in analyzing drought's geographical and temporal distribution. In this study, the semi-arid nature of the North-Eastern Dry Zone of Karnataka, characterized by low rainfall and high temperature, was examined for its vulnerability to drought. The Google Earth Engine (GEE) platform was utilized, which provides cloud-based access to advanced computational resources for processing multi-temporal satellite data. This approach minimizes the need for extensive data downloads and complex software operations, enabling efficient drought monitoring. In this study, GEE was applied to create and execute customized scripts for drought assessment, thereby accelerating the procedure and minimizing the need for extensive data downloads and complex software operations. The study focused on the North Eastern Dry Zone of Karnataka, particularly Kalaburgi district, employing the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) derived from MODIS data. The analysis revealed severe drought conditions, particularly in 2001, with NDVI values as low as 0.07 at Afzalpur station and 0.06 at Chitapur station, indicating significant vegetation stress. The VCI analysis further supported these findings, with values as low as 0.05 at Afzalpur station and 0.03 at Chitapur station, highlighting the drought's intensity. This integrated approach provides a reliable evaluation of agricultural drought, essential for enhancing drought management and mitigation strategies in the region.

Conceptualizing the Nepalese ‘Gang’

Abstract This article discusses the ‘gang’ in Nepal. Drawing on a two-year case study conducted in Kathmandu, involving a survey and interviews with criminal justice practitioners, government officials, NGO workers, social activists as well as interviews and focus groups with ex-street children, the article argues that gangs in Nepal are a complex social phenomenon. The term ‘gang’ is used to describe a multitude of criminal groups, many of which operate in alliance with the state. Gangs are also closely associated with organized crime and in many instances, street children become involved in these criminal groups. The article illustrates how street children develop their own groups to assist their survival. For these young people gangs provide solidarity, support, friendship and are vehicles for income generation. Thus, there is no one ‘gang’, rather many gangs, who change and evolve, the implications of which are discussed.

Contamination Characterization, Toxicological Properties, and Health Risk Assessment of Bisphenols in Multiple Media: Current Research Status and Future Perspectives

Bisphenols (BPs) are ubiquitous environmental endocrine disruptors that cause various human health hazards and pollute water, soil, and the atmosphere to varying degrees. Although various studies have investigated the pollution characteristics and health hazards of BPs in different media, a systematic review of BPs in the broader environmental context is still lacking. This study highlights the pollution characteristics, detection methods, and risk assessment status of BPs by combining relevant studies from both domestic and international sources, and their environmental distribution characteristics are summarized. The results show that BP pollution is a widespread and complex global phenomenon. Bisphenol A (BPA) remains the predominant component of BPs, which can damage the nervous and reproductive systems. At present, high-performance liquid chromatography–tandem mass spectrometry, high-performance liquid chromatography, and liquid chromatography–tandem mass spectrometry are the main detection methods used for BPs. BPs can also damage the reproductive system, leading to germ cell apoptosis and ovarian damage. Future research should focus on expanding the BP testing repertoire, advancing rapid detection techniques, elucidating toxic mechanisms, conducting comprehensive safety assessments, and developing systematic health risk assessment methods. These efforts will provide a scientific foundation for preventing and controlling emerging pollutants.

Impacts of drought on respiratory mortality in the upper midwest United States: a population subgroup assessment

Abstract Background: Drought is a complex climate phenomenon that varies regionally and temporally across the globe. Its frequency and intensity are expected to increase due to climate change, posing significant threats to human health. Despite these risks, there remains a notable gap in research on the health impacts of drought, particularly at the regional level within the United States.
Objective: This study aims at assessing the risk of severe drought exposure to respiratory mortality in the Upper Midwest of the United States among the general population and at-risk subgroups from 2000 to 2018. 
Method: Respiratory mortality was obtained for each county and month during study period based on the 10th International Classification of Disease (ICD) codes. We used the Evaporative Demand Drought Index (EDDI) categorized into three levels and analyzed it at two timescales: 6 months and 12 months. A Bayesian two-stage time-series modeling approach was applied to estimate the county-level and overall risk (OR) along with the 95% Credible Intervals (Cr). Stratification analyses were conducted by age group, gender, race, urbanicity, and state within the region.
Results: Statistically significant associations were identified between respiratory mortality and the severe droughts. The estimated risks were 1.151 (95% Cr: 1.126 to 1.178) and 1.092 (95% Cr: 1.063 to 1.120) for the 6- and 12-month severe droughts. Our study suggests that the risks differs by subgroups: elevated risks in elderly (higher 14.4% and 9.8% for 6- and 12-month), White (15.1% and 9.0% higher for 6- and 12-month), males (12.7% and 5.1% higher for 6- and 12-month), females (15.8% and 11.1% higher for 6- and 12-month), urban 15.8% and 8.8% higher for 6- and 12-month), and rural (13.6% and 9.9% higher for 6- and 12-month) communities. Iowa showed the greatest increase (23.1%) in the 6-month severe drought, while Minnesota had the highest 20.3% in the 12-month severe drought.
Conclusion: Our findings suggested that the elderly, both males and females, both rural and urban areas were the most affected subgroups. These findings demonstrate the need for policymakers and communities to adopt more effective drought mitigation strategies in this region. 

Exploring the Silent Connection Between Sleep Disorders (i.e., OSA, Insomnia) and Cardiovascular Diseases: Pathophysiology and Insights

Sleep is a complex physiological phenomenon crucial for health. Despite this, millions suffer from sleep disorders, contributing to a range of health issues, particularly cardiovascular diseases (CVD). The pathophysiological mechanisms linking sleep disorders, such as insomnia and obstructive sleep apnea (OSA), to cardiovascular risk factors include disruptions in inflammatory, autonomic, and metabolic pathways. Increased sympathetic nervous system activity, chronic inflammation, and metabolic dysregulation stemming from poor sleep can lead to conditions like hypertension, obesity, and insulin resistance, significantly elevating the risk for CVD. This article reviews the connection between sleep quality and cardiovascular disease risks. Sleep disorders (i.e., insomnia and OSA) have been found to impact cardiovascular risk factors adversely. Studies have found an association between abnormal sleep and increased cardiovascular morbidity and mortality by higher risks of hypertension, diabetes, obesity, and dyslipidemia. The review also discusses non-pharmacological interventions, such as relaxation training, Cognitive behavioral therapy for insomnia (CBT-I), and red-light therapy, which have shown efficacy in improving sleep quality and reducing cardiovascular risks. Dual orexin receptor antagonists and Ashwagandha promise to enhance sleep quality and cardiovascular health, but further research is needed. Addressing sleep disorders and promoting healthy sleep practices are essential for mitigating the global burden of cardiovascular diseases, underscoring the need for continued research and effective public health interventions.

The role of psychosocial factors in performing hurt: a systematic review covering the high-performance domains of sport, music, and dance

ABSTRACT Performing hurt is a complex but common phenomenon among high-performing athletes, musicians, and dancers. Previous research has analyzed this health risk behavior from different perspectives, predominantly focusing on psychological aspects and the influence of the social and cultural context. The diversity of existing research underscores the need for a synthesis informed by a psychosocial approach. With this systematic review, we aimed to synthesize research on the role of psychosocial factors in performing hurt. Forty studies from three high-performance domains, namely sports, music, and dancing, were included. Our convergent thematic analysis identified two overarching psychosocial factors from current research that contributed to performing hurt: (1) Personal characteristics of the high-performer such as performance identity or fearing the consequences of not performing hurt, and (2) Characteristics of the high-performance setting such as the sociocultural trivialization of pain and injuries or intensive practice and competition schedules. Whereas most included studies examined the role of psychological and social factors in performing hurt separately, we highlight the potential interplay between characteristics of the high-performer and the high-performance setting. Future performing hurt research should focus on these interconnections, as such knowledge might help to identify high-performers at risk and organizational factors that contribute to performing hurt.

Mitigating errors in analog quantum simulation by Hamiltonian reshaping or Hamiltonian rescaling

Simulating quantum many-body systems is crucial for advancing physics but poses substantial challenges for classical computers. Quantum simulations overcome these limitations, with analog simulators offering unique advantages over digital methods, such as lower systematic errors and reduced circuit depth, making them efficient for studying complex quantum phenomena. However, unlike their digital counterparts, analog quantum simulations face significant limitations due to the absence of effective error mitigation techniques. This work introduces two novel error mitigation strategies—Hamiltonian reshaping and Hamiltonian rescaling—in analog quantum simulation for tasks like eigen-energy evaluation. Hamiltonian reshaping uses random unitary transformations to generate new Hamiltonians with identical eigenvalues but varied eigenstates, allowing error reduction through averaging. Hamiltonian rescaling mitigates errors by comparing eigenvalue estimates from energy-scaled Hamiltonians. Numerical calculations validate both methods, demonstrating their significant practical effectiveness in enhancing the accuracy and reliability of analog quantum simulators.