Response Strategies Research Articles

Risk evolution and prevention and control strategies in emergency responses for Arctic maritime transportation

The intertwined and complex risks involved in the various stages of the Arctic maritime transportation emergency response severely hinder the response's effectiveness. The present study aims to develop a risk coupling network involved in the Arctic maritime transportation emergency response. First, an Arctic maritime transportation emergency response scenario is defined by reviewing SAR manuals and agreements, emergency guidelines of ships, and emergency drill videos. Second, the control structure of the emergency response process is systematically analyzed through Systems-theoretic process analysis, and a risk coupling network with task-layer, unsafe control actions-layer, and risk-layer is developed. Third, the robustness of the risk coupling network is stimulated by random and deliberate attacks, where deliberate attacks are based on the measured results of node degree, betweenness centrality, and integrated values. Finally, critical risk factors are determined based on the results of the network robustness analysis, and targeted prevention and control strategies of critical risk factors for the Arctic maritime transportation emergency response are proposed. The research findings suggest that equipment/system failures, personnel experience and expression capabilities, the effects of high-latitude magnetic fields, response times to assistance requests, and the preparedness of emergency resources should be focused on during Arctic maritime transportation emergency response.

Molecular pathways of osmoregulation in response to salinity stress in the gills of the scalloped spiny lobster (Panulirus homarus) within survival salinity

Scalloped spiny lobster (Panulirus homarus) aquaculture is the preferred strategy to resolve the conflict between supply and demand for lobster. Environmental conditions, such as salinity, are key to the success of lobster aquaculture. However, physiological responses of P. homarus to salinity stress have not been well studied. This study investigated the gill histology, osmoregulation and gill transcriptome of the early juvenile P. homarus (weight 19.04 ± 3.95 g) cultured at salinity 28 (control), 18, and 38 for 6 weeks. The results showed that the gill filaments of P. homarus exposed to low salinity showed severe separation of the cuticle and epithelial cells due to water absorption and swelling, as well as the dissolution and thinning of the cuticle and the rupture of the septum that separates the afferent and efferent channels. The serum osmolarity of P. homarus varied proportionately with external medium salinity and remained consistently above ambient osmolarity. The serum Na+, Cl−, K+, and Mg2+ concentrations P. homarus exhibited a pattern similar to that of serum osmolality, while the concentration of Ca2+ remained unaffected at salinity 18 but significantly increased at salinity 38. Gill Na+/K+-ATPase activity of P. homarus increased (p < 0.05) under the both salinity stress. Salinity 18 significantly increased Glutamate dehydrogenase (GDH) and Glutamicpyruvic transaminase (GPT) activity in the hepatopancreas of P. homarus (p < 0.05). According to transcriptome analysis, versus control group (salinity 28), 929 and 1095 differentially expressed genes (DEGs) were obtained in the gills of P. homarus at salinity 18 and 38, respectively, with these DEGs were mainly involved in energy metabolism, transmembrane transport and oxidative stress and substance metabolism. In addition, the expression patterns of 8 key DEGs mainly related to amino acid metabolism, transmembrane transport and oxidative stress were verified by quantitative real-time PCR (RT-qPCR). The present study suggests that salinity 18 has a greater impact on P. homarus than salinity 38, and P. homarus demonstrates effective osmoregulation and handle with salinity fluctuations (18 to 38) through physiological and functional adaptations. This study provides an improved understanding of the physiological response strategies of P. homarus facing salinity stress, which is crucial for optimizing aquaculture practices for this species.

A Computational Approach for Designing and Validating Small Interfering RNA against SARS-CoV-2 Variants.

The aim of this study is to develop a novel antiviral strategy capable of efficiently targeting a broad set of SARS-CoV-2 variants. Since the first emergence of SARS-CoV-2, it has rapidly transformed into a global pandemic, posing an unprecedented threat to public health. SARS-CoV-2 is prone to mutation and continues to evolve, leading to the emergence of new variants capable of escaping immune protection achieved due to previous SARS-CoV-2 infections or by vaccination. RNA interference (RNAi) is a remarkable biological mechanism that can induce gene silencing by targeting complementary mRNA and inhibiting its translation. In this study, using the computational approach, we predicted the most efficient siRNA capable of inhibiting SARS-CoV-2 variants of concern (VoCs). The presented siRNA was characterized and evaluated for its thermodynamic properties, offsite-target hits, and in silico validation by molecular docking and molecular dynamics simulations (MD) with Human AGO2 protein. The study contributes to the possibility of designing and developing an effective response strategy against existing variants of concerns and preventing further.

Impact of Therapeutic Inertia on Patient-Reported Outcomes in Moderate-to-Severe Atopic Dermatitis: A 12-Month Longitudinal Study from the TARGET-DERM AD Registry

Background: Therapeutic inertia, the delay or reluctance to modify treatment when goals are unmet, is a significant challenge in managing chronic diseases, including atopic dermatitis (AD). This inertia can lead to suboptimal disease control and affect patient outcomes. Objectives: This study evaluates the effect of therapeutic inertia on patient-reported outcomes (PROs) in moderate-to-severe AD patients undergoing systemic treatment over 3 to 12 months. Methods: We analyzed longitudinal data from the TARGET-DERM AD registry, which includes 3,457 patients with moderate-to-severe AD from 39 centers across the U.S. and Canada. Eligible patients had documented patient-reported outcomes (PROs) at the initiation of systemic therapy and at subsequent 3-month intervals up to 12 months of follow-up. We assessed the proportion of patients not meeting treatment targets based on expert consensus. Patients had a validated Investigator Global Assessment (vIGA-AD) score of 3 or more at initiation of either an advanced systemic therapy (AST) such as biologics or JAK inhibitors or a conventional systemic therapy (CST) such as cyclosporine, methotrexate, or prednisone. PROs were evaluated at 3-month intervals up to 12 months, assessing achievement against the predefined treatment targets. PRO measures included Worst-Itch (PROMIS Itch-Severity), POEM, PO-SCORAD, NRS-sleep, and NRS-pain with specific moderate and optimal target levels established for each. Itch-Severity (range: 0–10; moderate target: ≥4-point reduction, optimal target: score ≤1), POEM (range: 0-28; moderate target: ≥4-point reduction, optimal target: score ≤2), PO-SCORAD (range: 0-103; moderate target: score ≤24; optimal target: score ≤10), NRS-sleep and NRS-pain (range: 0-10; moderate target: reduction ≥3-points; optimal target: score ≤1). Results: Out of 2107 patients with moderate-to-severe AD, 445 qualifying participants were included (63.8% adult, 62.0% female, 45.4% Non-Hispanic White, mean age of 31 years). Most patients (88.8%) initiated AST, with dupilumab being the most common (86.5%). At 6 months, significant proportions of AST-treated patients failed to reach moderate and optimal targets for itch (67% and 79%, respectively), POEM (46% and 69%), and NRS-sleep (59% and 47%). By 12 months, these figures were similar, with 66% and 88% failing to meet itch targets, 53% and 73% failing to meet POEM targets, and 62% and 45% failing to meet NRS-sleep targets, respectively. A similar pattern was observed for other PROs. CST-treated patients exhibited similar trends. Conclusions: The study highlights the profound impact of therapeutic inertia on the quality of life of patients with moderate-to-severe AD. Despite systemic therapy, a considerable proportion failed to meet treatment targets over a 12-month period, underscoring the need for more proactive and responsive treatment strategies in AD management.

Transforming Aviation's Impact on the Climate: Rethinking the Research Strategy.

Aviation is under tremendous pressure to mitigate its impacts on the climate, but the best response strategies are unknown today due to deep uncertainties. How low-emission fuels will scale to levels relevant for the industry, along with the best strategies for managing contrails and other non-CO2 effects, are unknowable today with unknown cost and disruption. A conventional risk-based approach that involves investment across a known set of options is unworkable; instead, we argue that an experimentalist approach is needed that addresses deep uncertainties head on. This hinges on four key factors: a critical mass of actors facing strong incentives to identify solutions, a wide search for alternatives through experiments, periodic assessments, and adjustment of goals and strategies. Present strategies do not give enough attention to higher-risk alternatives with disruptive potential, because those approaches have few political and organizational supporters. Small groups of highly motivated actors─such as the nascent coalition of first movers on clean aviation already forming in Europe and the U.S.─could initiate an experimentalist program. The challenges of the aviation sector mirror other hard-to-abate sectors, making this framework potentially applicable to a wider set of sectors where technological, business, and investment choices are shrouded in deep uncertainty.

The Potential of Drug Repurposing as a Rapid Response Strategy in COVID-19 Therapeutics

Drug repurposing has emerged as a promising strategy in the rapid development of effective therapeutics for COVID-19. This approach leverages existing medications, previously approved for other indications, to target the pathophysiological mechanisms of SARS-CoV-2 infection. Several drugs were tested during the COVID-19 pandemic, developed originally for other purposes and under less-than-ideal conditions. Some of the most well-known include remdesivir, an Ebola drug approved by the FDA for emergency use to treat COVID-19, and dexamethasone, a corticosteroid that reduces death associated with severe infection through immunomodulation. However, while hydroxychloroquine and ivermectin, among others, showed very meager or no benefit, it is clear that such early promise must be subjected to firm testing. Despite such promises, drug repurposing may face several inconsistent clinical outcomes, questions over safety, and the inability to address all forms of COVID-19 pathology. Key candidates identified through high-throughput screening and computational methods include antiviral agents, anti-inflammatory drugs, and those targeting host cell pathways critical for viral replication. This review discusses the efficacy and mechanisms of these repurposed drugs, highlights ongoing clinical trials, and addresses challenges such as resistance and optimal dosing. Ultimately, drug repurposing represents a crucial component of the multi-faceted response required to combat the COVID-19 pandemic effectively.

Understanding community resilience during the drinking water contamination event on Oahu, Hawaii, 2021-2022: a mixed mode approach.

A petroleum leak into the Joint Base Pearl Harbor-Hickam water system on Oahu, Hawaii in November 2021 contaminated the drinking water of approximately 93,000 users, causing many to relocate for months. Perceptions of health and wellbeing were captured using the Centers for Disease Control/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Assessment of Chemical Exposures (ACE) cross-sectional survey in collaboration with the Hawaii Department of Health (HDOH). Responses from the ACE online survey of community members, businesses, schools, health care and veterinary care organizations during the contamination event, containing quantitative questions and qualitative information from an open text field, were analyzed. Separately, a qualitative key informant questionnaire was administered to community establishments. Thematic content analysis was used to analyze and identify prominent themes from the ACE open text field and the key informant responses that were triangulated by the quantitative data when the themes aligned. Six major themes of disruption, communication, trust, stress, support, and ongoing needs were identified. Burdensome logistics from obtaining alternate water, negative financial impacts from relocation or losing business, distrust of information, perceived lack of support from response entities and uncertainty of long-term health impact caused significant disruption, stress and mental health. Individuals reported needing water, shelter, and mental health care while establishments wanted financial reimbursement and a resolution. The findings show that environmental disasters have significant disruptive and mental health impacts from stress. Identified themes can inform and improve emergency response and communication strategies and increase trust with community members during and after large chemical exposure events.

Robust Human Activity Recognition for Intelligent Transportation Systems Using Smartphone Sensors: A Position-Independent Approach

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies.

Modeling of Wildfire Digital Twin: Research Progress in Detection, Simulation, and Prediction Techniques

Wildfires occur frequently in various regions of the world, causing serious damage to natural and human resources. Traditional wildfire prevention and management methods are often hampered by monitoring challenges and low efficiency. Digital twin technology, as a highly integrated virtual simulation model, shows great potential in wildfire management and prevention. At the same time, the virtual–reality combination of digital twin technology can provide new solutions for wildfire management. This paper summarizes the key technologies required to establish a wildfire digital twin system, focusing on the technical requirements and research progress in fire detection, simulation, and prediction. This paper also proposes the wildfire digital twin (WFDT) model, which integrates real-time data and computational simulations to replicate and predict wildfire behavior. The synthesis of these techniques within the framework of a digital twin offers a comprehensive approach to wildfire management, providing critical insights for decision-makers to mitigate risks and improve emergency response strategies.

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks.

Trends of Climate Extremes and Their Relationships with Tropical Ocean Temperatures in South America

South America has experienced significant changes in climate patterns over recent decades, particularly in terms of precipitation and temperature extremes. This study analyzes trends in climate extremes from 1979 to 2020 across South America, focusing on their relationships with sea surface temperature (SST) anomalies in the Pacific and Atlantic Oceans. The analysis uses precipitation and temperature indices, such as the number of heavy rainfall days (R10mm, R20mm, R30mm), total annual precipitation (PRCPTOT), hottest day (TXx), and heatwave duration (WSDI), to assess changes over time. The results show a widespread decline in total annual precipitation across the continent, although some regions, particularly in the northeast and southeast, experienced an increase in the intensity and frequency of extreme precipitation events. Extreme temperatures have also risen consistently across South America, with an increase in both the frequency and duration of heat extremes, indicating an ongoing warming trend. The study also highlights the significant role of SST anomalies in both the Pacific and Atlantic Oceans in driving these climate extremes. Strong correlations were found between Pacific SST anomalies (Niño 3.4 region) and extreme precipitation events in the northern and southern regions of South America. Similarly, Atlantic SST anomalies, especially in the Northern Atlantic (TNA), exhibited notable impacts on temperature extremes, particularly heatwaves. These findings underscore the complex interactions between SST anomalies and climate variability in South America, providing crucial insights into the dynamics of climate extremes in the region. Understanding these relationships is essential for developing effective adaptation and mitigation strategies in response to the increasing frequency and intensity of climate extremes.

Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI

This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. The primary focus is on evaluating the applicability and performance of these forecasting models in predicting electricity consumption patterns, which is a critical component for implementing effective demand response (DR) strategies. The study provides a comprehensive analysis of the predictive accuracy, computational efficiency, and scalability of each algorithm using a dataset of real-time electricity consumption collected from AMI systems over a designated period. Through extensive experiments, we demonstrate that each algorithm has distinct strengths and weaknesses depending on the characteristics of the dataset. Specifically, SVM exhibited superior performance in handling nonlinear patterns and high volatility, while SARIMA effectively captured seasonal trends. LSTM showed potential in modeling complex temporal dependencies but was sensitive to hyperparameter settings and required a substantial amount of training data. This research offers practical guidelines for selecting the optimal forecasting model based on data characteristics and application needs, contributing to the development of more efficient and dynamic energy management strategies. The findings highlight the importance of integrating advanced forecasting techniques into smart grid systems to enhance the reliability and responsiveness of DR programs. This study lays a solid foundation for future research on integrating these forecasting models into real-world AMI applications to support effective demand response and grid stability.

Balancing Standardization and Ecological Validity in the Measurement of Social Communication Intervention Outcomes.

Caregiver-mediated communication intervention outcomes are inconsistently measured, varying by assessment settings, materials, and activities. Standardized materials are often used for measuring outcomes, yet it remains unknown whether such standardized contexts equitably capture caregiver and child intervention outcomes representative of dyads' typical interactions. This within-subject study investigates how intervention outcomes differ between family-selected and standardized interactional contexts for autistic toddlers and their caregivers. Following an 8-week caregiver-mediated telehealth intervention delivered to 22 dyads, caregiver outcomes (fidelity of using responsive communication facilitation strategies) and child outcomes (total spontaneous directed communicative acts) were measured during two interactional contexts using (a) family-selected activities and (b) a standardized toy set. A routines checklist surveyed the activities dyads value, enjoy, complete frequently, and/or find difficult with their child. Caregiver outcomes and child outcomes did not significantly differ between the family-selected and standardized interactional contexts. Descriptive results suggest that the types of toys commonly included in standardized toy sets are representative of the materials many families choose when playing with their child at home. However, during the family-selected interactional context, the majority of dyads also chose materials or activities that were not available to them during the standardized context. It is necessary to carefully consider a more expansive approach to standardization in which intervention outcomes are measured in ecologically valid contexts, which meaningfully, accurately, and equitably capture caregiver and child functional outcomes, and the translation of interventions to families' everyday routines.

Rebranding as a Crisis Response Strategy: A Stakeholder Perspective

Rebranding as a Crisis Response Strategy: A Stakeholder Perspective

Resilience through Mobility: Urban Residents’ Adaptation Strategies in Conflict-Affected Bamenda, Cameroon

The global phenomenon of human population displacement during environmental stressors and socio-political crises has far-reaching impacts. In urban areas, residents' mobility is often driven by various factors. This study investigates urban residents' mobility as a resilience strategy in response to the Anglophone socio-political crisis in Bamenda, Cameroon. Employing a case study design and purposive sampling, the research selected five neighborhoods based on the extent of residential changes as a result of the ongoing socio-political crisis in the region. Data were collected through semi-structured interviews with 105 respondents and focus group discussions to explore residential mobility as a resilience mechanism. The findings revealed that five specific population groups were affected by the socio-political crisis, prompting them to relocate within Bamenda. Among those displaced, 60% were households led by individuals aged 26-32. Key reasons for displacement included ongoing violence, loss of life, family disconnection, property destruction, disrupted economic activities, halted education, and increased internally displaced persons in the region. The study also found that residents with higher financial status could relocate to safer areas, while vulnerable individuals often fled or temporarily sought refuge in forests. The prolonged crisis, over seven years now, has severely impacted Bamenda residents, escalating forced displacement rates. Consequently, the study recommends that the government implement targeted urban planning policies to enhance resilience strategies, focusing on affordable social housing, resettlement structures, and improved access to basic services for displaced residents, while fostering peace and stability in Cameroon’s Northwest and Southwest regions.

The Impact of Bamboo (Phyllostachys edulis) Expansion on the Water Use Patterns of Broadleaf Trees

The expansion of bamboo (Phyllostachys edulis) affects the growth status of trees in colonized forests, but there has been insufficient research on changes in tree water physiology. In this study, we used stable δ2H, δ18O, and 13C isotope ratios to analyze the water sources and water use efficiency (WUE) of bamboo, deciduous broadleaf trees (Alniphyllum fortunei), and evergreen broadleaf trees (Machilus pauhoi and Castanopsis eyrei) in a bamboo-expended broadleaf forest (BEBF), a bamboo-absent broadleaf forest (BABF), and a bamboo forest (BF). We found that the expansion of bamboo had no significant effect on the water sources and WUE of deciduous broadleaf trees, but altered the water sources of evergreen broadleaf trees. During the growing season, evergreen broadleaf trees decrease their uptake fractions of surface soil water by 7.1% to 9.6% and increased their uptake fractions of middle soil water by 5.8%~9.4%. Conversely, during the non-growing season, they increased their uptake fractions of surface soil water by 11.9% and decreased their uptake fractions of deeper soil water by 5.6%~12.9%. Additionally, after expanding into broadleaf forests, bamboo increased its uptake proportion of surface and shallow soil water by 20.0% and 9.4% during the growing season. Its WUE also improved, increasing by 20.0 μmol/mol and 13.0 μmol/mol during the growing and non-growing seasons, respectively. These results indicate that as bamboo expands into broadleaf forests, it enhances its competitiveness for water resources by changing its water use strategy. Compared to deciduous broadleaf trees, evergreen broadleaf trees exhibit more flexible water use strategies under the conditions of bamboo expansion. Our research reveals, for the first time, how broadleaf trees adjust their water use strategies in response to bamboo expansion, and uncovers the mechanisms behind bamboo expansion into evergreen broadleaf forests from the perspective of water use strategies. This will aid future forest management under the conditions of bamboo expansion.

Research on the Index Calculation Method for the Impact of Drought on Water Quality in the Nakdong River, Korea

The impact of drought is intensifying due to climate change, leading to significant environmental consequences, particularly concerning river water quality. While drought is typically classified as meteorological or hydrological, studies assessing its environmental impacts remain limited. Drought-induced hydrological alterations in rivers often degrade water quality, necessitating the development of an environmental drought index. This study introduces a novel methodology for calculating an index to evaluate the effects of drought on river water quality, specifically applied to tributaries of the Nakdong River in South Korea. The index was constructed by reviewing existing water quality and drought indices, selecting relevant parameters, and weighting each factor following the National Sanitation Foundation Water Quality Index (NSFWQI) methodology. Factors integrated into the index encompass both meteorological and hydrological indicators, with priority given to variables measurable in real time. Real-time parameters—such as flow rate, cumulative precipitation, days without rainfall, and sensor-based metrics (pH, electrical conductivity [EC], dissolved oxygen [DO], and total organic carbon [TOC])—were incorporated. Additionally, for rivers with upstream dams, dam discharge data were included to reflect its influence on flow conditions. The applicability of the calculated index was assessed by comparing index values to observed water quality data. A class interval structure was implemented to enhance the index’s usability across diverse riverine conditions. Furthermore, the utility of the index was validated by comparing it to the basin’s target water quality, thereby assessing its sensitivity to drought-induced water quality deterioration. The environmental drought index proposed in this study enables the proactive and real-time monitoring of water quality under drought conditions. When applied to 10 tributaries of the Nakdong River, the index demonstrated a clear correlation between drought conditions and water quality deterioration. This index provides a practical tool for river management, facilitating early response strategies to mitigate water quality impacts associated with environmental drought.

Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities

Abstract Enhancing resilience against seismic hazards in earthquake-prone regions is essential for reducing the devastating impacts of disasters. Seismic resilience refers to a community’s ability to withstand and recover from earthquake impacts, while preparedness gaps are the areas where current measures are insufficient to effectively respond to or mitigate earthquake damage. This study focuses on Montenegro – a region with frequent seismic activity – aiming to assess resilience levels, identify critical gaps in preparedness, and evaluate the effectiveness of existing response strategies. Using qualitative methods, including semi-structured interviews, the research gathered insights from residents of Montenegro’s most vulnerable cities: Nikšić, Podgorica, Bar, Kotor, Cetinje, Budva, Herceg Novi, and Berane. Participants, chosen for their first-hand experience with significant earthquake impacts, provided valuable perspectives on various aspects of resilience, from local government response to individual preparedness. This research revealed significant disparities in resilience across communities: for instance, approximately 62.5% of the respondents highlighted inadequate education as a barrier to effective earthquake preparedness, and only 37.5% reported awareness of basic earthquake response procedures. Furthermore, while some communities, such as urban areas with accessible services, reported higher preparedness levels, rural areas showed deficiencies, with 50% of the respondents from these areas identifying a lack of organized drills and limited public awareness initiatives. These findings underscore the urgent need for community-specific preparedness programs and enhancements in both structural resilience and public education to bolster community readiness effectively. Also, findings highlight the need for customized preparedness programs tailored to specific community needs, alongside improvements in structural safety measures and educational outreach. The study underscores the importance of a comprehensive approach involving detailed risk assessments, community-focused preparedness training, and stronger public awareness initiatives. Furthermore, the study calls for enhanced local government capabilities to sustain proactive response measures, including rapid mobilization of emergency resources and regular disaster simulations, to build long-term resilience across communities.

Unveiling ancient Jerusalem’s pastoral dynamics (7th to 2nd centuries BCE) with multi-isotope analysis

This study explores changes in pastoral practices in the Jerusalem region (Iron Age II - Late Hellenistic) through a multi-isotope approach (strontium, carbon, oxygen, and nitrogen). Based on the analysis of 135 sheep, goat, and cattle teeth and bone samples from Givati Parking Lot we demonstrate the value of this method in reconstructing past animal husbandry, revealing adaptation and resilience of pastoral communities amidst environmental and socio-political changes. Isotopic analysis indicates local sourcing for most animals, with intriguing outliers from distant regions up to 150 km away, suggesting regional exchange networks. Notably, the Persian period (5th century BCE) exhibits a wider isotope range, implying increased flexibility and exploitation of diverse grazing lands, potentially driven by climate shifts and political upheavals. Conversely, Late Hellenistic (2nd century BCE) livestock display restricted movement, while showcasing a rise in desert caprines, indicative of increased import compared to the Persian era. These findings highlight the dynamism and adaptability of past pastoral communities, adjusting their strategies in response to various pressures. This study opens new avenues for understanding human-environment interactions in the Levant and underscores the power of multi-isotope approaches in unraveling intricate socio-economic and ecological dynamics of the past.

Parenting Under Pressure: Associations between Perceived Social Pressure and Parental Involvement among Mothers and Fathers

AbstractIn many Western countries, the ideology of intensive parenting has gained prominence in the discourse of experts, policymakers, and within popular culture. This ideology emphasizes deep parental involvement in emotional, physical, and financial aspects (Lee et al., 2014). Meeting these demanding standards can exert significant pressure on parents, especially on mothers often considered as the primary caregiver. Moreover, these pressures may prompt parents to be highly, and potentially overly, involved in their children’s lives. Using data from 146 parent dyads (N = 292 parents; Mage = 47.57 years) of Swiss adolescents, the study explores parental perceptions of pressure to be a perfect parent and its association with one positive (responsiveness) and two negative types of involvement (overprotection and overvaluation). Thereby, we estimated Actor-Partner Interdependence Models (APIM) to examine mutual influences between mothers and fathers. The results indicated that mothers reported experiencing significantly more pressure than fathers. We found evidence for a positive association between perceptions of pressure and parental overprotection among both parents. The results also showed that there was a significant association between feelings of pressure and overvaluation, but only among fathers. Associations between pressure and responsiveness were not significant, and no significant partner effects were observed in any of the models. In conclusion, mothers particularly face heightened pressure to be perfect parents, but both parents may adapt their parenting strategies in response to perceived pressure to be perfect as a parent. These findings highlight the potential issues associated with societal pressures on parents and their impact on parenting behavior.