Quantifying future climate extreme indices: implications for sustainable urban development in West Africa, with a focus on the greater Accra region.

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa.

Range dynamics of Anopheles mosquitoes in Africa suggest a significant increase in the malaria transmission risk.

Despite a more than 100-year effort to combat malaria, it remains one of the most malignant infectious diseases globally, especially in Africa. Malaria is transmitted by several Anopheles mosquitoes. However, until now few studies have investigated future range dynamics of major An. mosquitoes in Africa through a unified scheme. Through a unified scheme, we developed 21 species distribution models to predict the range dynamics of 21 major An. species in Africa under future scenarios and also examined their overall range dynamic patterns mainly through suitability overlap index and range overlap index. Although future range dynamics varied substantially among the 21 An. species, we predicted large future range expansions for all 21 An. species, and increases in suitability overlap index were detected in more than 90% of the African continent for all future scenarios. Additionally, we predicted high range overlap index in West Africa, East Africa, South Sudan, Angola, and the Democratic Republic of the Congo under future scenarios. Although the relative impacts of land use, topography and climate variables on the range dynamics depended on species and spatial scale, climate played the strongest roles in the range dynamics of most species. Africa might face an increasing risk of malaria transmissions in the future, and better strategies are required to address this problem. Mitigating climate change and human disturbance of natural ecosystems might be essential to reduce the proliferation of An. species and the risk of malaria transmissions in Africa in the future. Our strategies against their impacts should be species-specific.

Alignment of port policy to the context of the Physical Internet.

The Physical Internet (PI) is a paradigm-changing and technology-driven vision, which is expected to significantly impact the development of the freight transport and logistics (FTL) system of today. However, the development of the FTL system towards the PI creates much uncertainty for its current stakeholders. Ports are one of those stakeholders that are expected to be profoundly affected by these developments. However, research that focuses on port policy, under the uncertain developments towards the PI, is still lacking. By providing port authorities with insights and recommendations on robust policy areas, we address this void in literature. We conduct a scenario analysis in combination with multi-criteria decision analysis (MCDA) to determine the importance of port performance indicators and policy areas in different scenarios. The most significant, uncertain, and orthogonal factors for the development of the PI are technological development and institutional development. We find that for a proper alignment with the PI vision, in three out of four scenarios, ports should prioritize the implementation of digital solutions and standards, as opposed to an infrastructure focused policy.

Returning to the Wilderness: Potential Habitat Suitability of Non-Native Pet Birds in South Africa.

The global trade of non-native pet birds has increased in recent decades, and this has accelerated the introduction of invasive birds in the wild. This study employed ensemble species distribution modelling (eSDM) to assess potential habitat suitability and environmental predictor variables influencing the potential distribution of non-native pet bird species reported lost and sighted in South Africa. We used data and information on lost and found pet birds from previous studies to establish and describe scenarios of how pet birds may transition from captivity to the wild. Our study revealed that models fitted and performed well in predicting the suitability for African grey (Psittacus erithacus), Budgerigar (Melopsittacus undulatus), Cockatiel (Nymphicus hollandicus), Green-cheeked conure (Pyrrhura molinae), Monk parakeet (Myiopsitta monachus), and Rose-ringed parakeet (Psittacula krameri), with the mean weighted AUC and TSS values greater than 0.765. The predicted habitat suitability differed among species, with the suitability threshold indicating that between 61% and 87% of areas were predicted as suitable. Species with greater suitability included the African grey, Cockatiel, and Rose-ringed parakeet, which demonstrated significant overlap between their habitat suitability and reported lost cases. Human footprint, bioclimatic variables, and vegetation indices largely influenced predictive habitat suitability. The pathway scenario showed the key mechanisms driving the transition of pet birds from captivity to the wild, including the role of pet owners, animal rescues, adoption practices, and environmental suitability. Our study found that urban landscapes, which are heavily populated, are at high risk of potential invasion by pet birds. Thus, implementing a thorough surveillance survey is crucial for monitoring and evaluating the establishment potential of pet species not yet reported in the wild.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms.

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

[Land Use Optimization and Carbon Reserve Assessment in the Shiyang River Basin].

Studying the response relationship and spatial distribution characteristics of carbon reserve and land use change and predicting the change trend of carbon reserve caused by the change of land use type in the future can provide some reference for watershed policy formulation, land use structure adjustment, and the realization of the "two-carbon" goal. Based on the land use data from 2000, 2010, and 2020, the InVEST model was used to calculate carbon reserves and analyze the change characteristics and to simulate the land use change and its impact on carbon reserves in natural development, urban development, and ecological protection in 2030 with the help of the PLUS model. The study found that ① the main land types in the Shiyang River Basin from 2000 to 2020 were cultivated land, grassland, and unused land. The area of cultivated land, water area, and construction land in the Shiyang River Basin showed a significant increasing trend, and the construction land area increased the most. ② In the natural development scenario of 2030, cultivated land, water area, and construction all increased by 6.15%, 9.56%, and 29.9%, respectively. In the urban development scenario, the area of construction land increased the most. Compared with that in the other two scenarios, the area of forest land and grassland increased in the ecological protection scenarios. ③ The carbon reserves of the Shiyang River Basin from 2000 to 2020 showed a steady increase, with an overall increase of 0.035×108 t. The increased carbon reserves were mainly due to the increase in cultivated land area. ④ In 2030, the carbon reserves of the Shiyang River Basin showed an increasing trend in all three scenarios. The carbon reserves in the three scenarios were 5.65×108, 5.64×108,and 5.73×108 t, respectively, with the largest increase in carbon reserves in the ecological conservation scenario, mainly due to the increase in grassland and woodland. The results showed that the expansion of construction land was the main cause of the loss of carbon reserves. If effective ecological protection measures are taken, the carbon reserves in the Shiyang River Basin will be improved, and the problem of the loss of carbon reserves caused by economic development can be solved.

Do Elite Basketball Players Maintain Peak External Demands throughout the Entire Game?

Consideration of workload intensity and peak demands across different periods of basketball games contributes to understanding the external physical requirements of elite basketball players. Therefore, the aim of this study was to investigate the average intensity and peak demands encountered by players throughout game quarters. PlayerLoad per minute and PlayerLoad at three different time samples (30 s, 1 min, and 3 min) were used as workload metrics. A total of 14 professional elite male basketball players were monitored during 30 official games to investigate this. A linear mixed model and Cohen's d were employed to identify significant differences and quantify the effect sizes among game quarters. The results showed a significant, moderate effect in PlayerLoad per minute between Q1 vs. Q4, and a small effect between Q2 and Q3 vs. Q4. Furthermore, a small to moderate decline was observed in external peak values for PlayerLoad across game quarters. Specifically,, a significant decrease was found for the 3 min time window between Q1 and other quarters. The findings from the present study suggest that professional basketball players tend to experience fatigue or reduced physical output as the game progresses.

An Assessment of the Climate Change Impacts on the Distribution of the Glacial Relict Woodpecker Three-Toed Woodpecker Picoides tridactylus.

The Three-Toed Woodpecker Picoides tridactylus is a rare and endangered woodpecker on the Balkan Peninsula. Despite being widely distributed in Northern Europe, its distribution on the Balkan Peninsula is limited to high-altitude forest habitats, where it represents a glacial relict. Assessing the climate change impacts on its distribution can be crucial for improving the conservation and future survival of this specialist species on the Balkan Peninsula. We used species distribution modelling (SDM) to identify its potential distribution in the past (last interglacial and last glacial maximum), present, and future (2050 and 2070). Our results indicate that this species had the greatest distribution during the last glacial maximum, after which its distribution contracted to areas where suitable environment persisted (high altitudes). The largest territory of the Balkan Peninsula has an unsuitable environment for the species to inhabit, while highly suitable habitats have the smallest share in the total area of suitable habitats. All future models show a decrease in the area of suitable habitats compared with the current period, indicating that global warming has a negative effect on the distribution of the species. We recommend that conservation activities must be of greater extent to ensure the species' survival in the Balkans.

Multi-scale assessment of rainwater harvesting availability across the continental U.S.

Addressing resilience, sustainability, and water resource conservation has become increasingly important in the modern world. Challenges arise due to periodic droughts, climate change, and seasonal variability in areas with limited freshwater availability. Therefore, implementing and promoting water reuse is essential. Rainwater harvesting (RWH) is one such alternative, offering benefits in conserving water resources and mitigating droughts while reducing urban flooding and costs by generating alternative lower-cost water sources. Providing users with knowledge of available volumes for harvesting, including homeowners and governmental entities, is key to encouraging this practice. Hydrological data and geographic information systems are fundamental for managing, designing, and projecting rainwater harvesting practices. However, no tools currently integrate this information at multiple scales with current and future climate scenarios. This research aimed to develop a multi-scale assessment tool named H2O HARVEST, for evaluating the availability and potential of rainwater harvesting. Additional benefits of the H2O HARVEST app include aiding decision-making by national governmental entities and analyzing potential future scenarios for homeowner users. The app also provides regulatory policy information at the state level. We offer an app with the necessary capabilities to bridge the technology gap and promote rainwater harvesting practices. Our research demonstrated that RWH has the potential to be a sustainable water reuse practice. For more than 50% of the states, the RWH could supply at least 50% of the water demand. The regions of the US with the greatest potential are the Central and East.

Dynamic shifts of functional diversity through climate-Resilient strategies and farmland restoration in a mountain protected area.

Land-use land-cover (LULC) change contributes to major ecological impacts, particularly in areas undergoing land abandonment, inducing modifications on habitat structure and species distributions. Alternative land-use policies are potential solutions to alleviate the negative impacts of contemporary tendencies of LULC change on biodiversity. This work analyzes these tendencies in the Montesinho Natural Park (Portugal), an area representative of European abandoned mountain rural areas. We built ecological niche models for 226 species of vertebrates (amphibians, reptiles, birds, and mammals) and vascular plants, using a consensus modelling approach available in the R package 'biomod2'. We projected the models to contemporary (2018) and future (2050) LULC scenarios, under four scenarios aiming to secure relevant ecosystem services and biodiversity conservation for 2050: an afforestation and a rewilding scenario, focused on climate-smart management strategies, and a farmland and an agroforestry recovery scenario, based on re-establishing human traditional activities. We quantified the influences of these scenarios on biodiversity through species habitat suitability changes for 2018-2050. We analyzed how these management strategies could influence indices of functional diversity (functional richness, functional evenness and functional dispersion) within the park. Habitat suitability changes revealed complementary patterns among scenarios. Afforestation and rewilding scenarios benefited more species adapted to habitats with low human influence, such as forests and open woodlands. The highest functional richness and dispersion was predicted for rewilding scenarios, which could improve landscape restoration and provide opportunities for the expansion and recolonization of forest areas by native species. The recovery of traditional farming and agroforestry activities results in the lowest values of functional richness, but these strategies contribute to complex landscape matrices with diversified habitats and resources. Moreover, this strategy could offer opportunities for fire suppression and increase landscape fire resistance. An integrative approach reconciling rewilding initiatives with the recovery of extensive agricultural and agroforestry activities is potentially an harmonious strategy for supporting the provision of ecosystem services while securing biodiversity conservation and functional diversity within the natural park.

Assessment of Arctic sea ice simulations in cGENIE model and projections under RCP scenarios.

Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the previous ideal performance of cGENIE model in other fields and notable features, we evaluated the model's skill in simulating Arctic sea ice using multiple methods and it demonstrates great potential and combined advantages. On this basis, we examined the direct drivers of sea-ice variability and predicted the future spatio-temporal changes of Arctic sea ice using the model under different Representative Concentration Pathways (RCP) scenarios. Further studies also found that Arctic sea ice concentration shows large regional differences under RCP 8.5, while the magnitude of the reduction in Arctic sea ice thickness is generally greater compared to concentration, showing a more uniform consistency of change.

Frequency and intensity of maximal and submaximal demanding scenarios in U19 professional soccer players.

This study examined the frequency of the most demanding scenarios (MDS) during official soccer matches using rolling averages over 1, 5, and 10 min. Forty-two Under-19 players from different positions (central defenders, full-backs, central midfielders, wide midfielders, offensive midfielders, and forwards) were monitored across 27 matches using GPS to track distance covered, high-speed running, sprint distance, accelerations, and decelerations. Intensity thresholds were established based on percentiles (0-25, 25-50, 50-75, 75-100, and ≥100). The main findings suggest that: (i) Peak Demands occur in less than 1% of all time windows and variables for all positions; (ii) Most efforts occur below peak demands, with around 95% for high-speed running and sprint distance, and 85% for accelerations, decelerations, and total distance; (iii) Significant differences in intensity distributions were found between positions, particularly at medium-low and high intensities. Regarding training prescription, relying solely on MDS may be limited, highlighting the need to supplement MDS with other metrics for a comprehensive understanding of match demands. This approach ensures better-informed training programs for soccer players.

The occurrence of peak demand events during soccer matches is infrequent. Therefore, understanding the frequency of efforts below the intensity threshold of the most demanding passages for each playing position and across various analysed variables and periods is of utmost importance.MDS might not be enough for effective soccer training planning. Complementing with additional metrics like GPS, tactical analysis, physiological data and psychological factors is essential for a comprehensive understanding of the game's demands and tailored training programmes.

Sustainable scenarios in a plants-rhizobacteria-plant consumers system are in risk when biotic or abiotic factors change.

The ecological relationship among plants, rhizobacteria and plant consumers has attracted the attention of researchers due to its implications in field crops. It is known that, the rhizosphere is occupied not only by rhizobacteria which grant benefits to the plants but also by bacteria which are detrimental for them. In this work, we construct and analyze a plants-rhizobacteria-plant consumers system. In the modeling process, it is assumed that there is a conditioned interaction between plants and bacteria in the rhizosfera such that there is a mutualistic relationship at low densities of rhizobacteria and the relationship is parasitic or competitive at higher densities of them. Benefits granted by rhizobacteria include mechanisms that increase the plant growth and defense mechanisms against plant consumers. From the analysis of the model and its simplified version, we show that scenarios of coexistence of all populations can occur for a wide range of values of the parameters which describe biotic or abiotic factors; however, these scenarios are in risk since scenarios of exclusion of species can occur simultaneously due to the presence of bistability phenomena. The results obtained can be useful for the decision makers to design interventions strategies on field crops when plant growth-promoting rhizobacteria are used.

Climate change and urban sprawl: Unveiling the escalating flood risks in river deltas with a deep dive into the GBM river delta.

River deltas, such as the Ganges-Brahmaputra-Meghna (GBM) delta, are highly vulnerable to flooding, exacerbated by intense human activities and rapid urban growth. This study explores the evolution of urban flood risks in the GBM delta under the combined impacts of climate change and urban expansion. Unlike traditional assessments that focus on a single flood source, we consider multiple sources-coastal, fluvial, and pluvial. Our findings indicate that future urban expansion will significantly increase flood exposure, with a substantial rise in flood risk from all sources by the end of this century. Climate change is the main driver of increased coastal flood risks, while urban growth primarily amplifies fluvial, and pluvial flood risks. This highlights the urgent need for adaptive urban planning strategies to mitigate future flooding and support sustainable urban development. The extreme high emissions future scenario (SSP5-8.5) shows the largest urban growth and consequent flood risk, emphasizing the necessity for preemptive measures to mitigate future urban flooding. Our study provides crucial insights into flood risk dynamics in delta environments, aiding policymakers and planners in developing resilience strategies against escalating flood threats.

A dataset of CMIP6-based climate scenarios for climate change impact assessment in Great Britain.

Climate change is a critical issue in the 21st century. Assessment of the impacts of climate change is beneficial for assisting advanced recommendations for adaptations. Climate change impact assessments require high quality local-scale climate scenarios. The future climate projections from Global Climate Models (GCMs) are problematic to use at local scale due to their coarse spatial and temporal resolution, and existing biases. It is important to have climate change scenarios based on the GCMs ensemble downscaled to local scale to account for inherent uncertainty in climate projections, and to have a sufficient large number of years to account for inter-annual climate variability and low frequency, but high impact, extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across Great Britain based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator, LARS-WG 8.0. The data set consists of climate scenarios of daily weather of 1,000 realizations of typical years for a baseline, and very near (2030) and near-future (2050) climates, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 8.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of daily weather for the baseline, as well as for very near and near-future climate change scenarios, are essential for estimating inter-annual variation, and for detecting low frequency, but high impact, extreme climatic events, such as heat waves, floods and droughts. The present dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessments of climate change and decision support for mitigation and adaptation to climate change.

[Dynamic Response of Aerobic Denitrification Bacteria to Water Quality in Baiyangdian Lake Under Different Hydrological Scenarios].

In order to explore the evolution law and driving mechanism of aerobic denitrification bacteria in Baiyangdian Lake under different hydrological scenarios, based on water quality survey and high-throughput sequencing technology, this study conducted a water quality factor analysis and aerobic denitrification bacteria α-diversity analysis, species composition, and network analysis. The results showed that the water body of Baiyangdian Lake was weakly alkaline, with the highest T and the lowest DO in the rainy season and the lowest T and the highest DO in the freezing season. There were significant differences between NH4+-N, NO2--N, NO3--N, TN, permanganate index, Fe, and Mn in Baiyangdian water under different hydrological scenarios (P < 0.01), and there was no significant difference in TP under different hydrological scenarios (P > 0.05). The largest category in water bodies under different hydrological scenarios was Proteobacteria, and the genera with a higher relative abundance were Magnetospirillum, Aeromonas, Pseudomonas, Azospirillum, and Bradyrhizobium. In addition, within the aerobic denitrifying bacteria community, there were significant differences in α-diversity (P < 0.001), with the highest abundance of microbial communities occurring during the freezing period, and the highest diversity and evenness of microbial communities during the dry and freezing periods. According to the RDA and Mantel analyses, the water quality driving factors of flora were different under different hydrological scenarios. The water quality driving factors of flora in the dry season were pH, NO3--N, NO2--N, and permanganate index; the driving factors of flora in the rainy season were pH, T, DO, NO2--N, and TP; the driving factors of flora in the normal season were NO2--N, Fe, and permanganate index; and the driving factors of flora in the freezing season were NO3--N and NONO2--N. Network analysis showed that there were temporal differences in species related to water quality driving factors. The genera related to water quality driving factors during the dry season were Magnetospirillum, Aeromonas, and Azoarcus, whereas the genera related to the rainy season were Magnetospirillum, Pseudomonas, and Aeromonas. The genera related to the normal season were Magnetospirillum, Pseudomonas, and Limnohabitans, and the genera related to the freezing period were Magnetospirillum, Azoarcus, and Pseudomonas. The relationship between key water quality factors (mainly T, DO, NO3--N, and permanganate index) and aerobic denitrification flora in different hydrological scenarios was gradually changing with time. In conclusion, the study on the evolution characteristics of aerobic denitrification bacteria in Baiyangdian Lake under different hydrological scenarios and the driving mechanism of environmental factors could provide a basis for understanding the evolution mechanism of aerobic denitrification bacteria in the natural environment.

Moderating AC Usage Can Reduce Thermal Disparity between Indoor and Outdoor Environments.

In the context of escalating urban heat events due to climate change, air conditioning (AC) has become a critical factor in maintaining indoor thermal comfort. Yet the usage of AC can also exacerbate outdoor heat stress and burden the electricity system, and there is little scientific knowledge regarding how to balance these conflicting goals. To address this issue, we established a coupled modeling approach, integrating the Weather Research and Forecasting model with the building energy model (WRF_BEP + BEM), and designed multiple AC usage scenarios. We selected Chongqing, China's fourth-largest megacity, as our study area due to its significant socioeconomic importance, the severity of extreme heat events, and the uniqueness of its energy infrastructure. Our analysis reveals that AC systems can substantially reduce indoor temperatures by up to 18 °C; however, it also identifies substantial nighttime warming (2-2.5 °C) and a decline in thermal comfort. Particularly for high-density neighborhoods, when we increase 2 °C indoors, the outdoor temperature can be alleviated by up to 1 °C. Besides, despite the limited capacity to regulate peak electricity demand, we identified that reducing the spatial cooled fraction, increasing targeted indoor temperature by 2 °C, and implementing temporal AC schedules can effectively lower energy consumption in high-density neighborhoods, especially the reduction of spatial cooled fraction (up to 50%). Considering the substantial demand for cooling energy, it is imperative to carefully assess the adequacy and continuity of backup energy sources. The study underscores the urgency of reassessing energy resilience and advocates for addressing the thermal equity between indoor and outdoor environments, contributing to the development of a sustainable and just urban climate strategy in an era of intensifying heat events.

Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities.

The causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables. However, particularly in the presence of hidden variables, many causal structures are compatible with the same set of independencies inferred from the marginal distributions of observed variables. Additional constraints allow further testing for the compatibility of data with specific causal structures. An existing family of causally informative inequalities compares the information about a set of target variables contained in a collection of variables, with a sum of the information contained in different groups defined as subsets of that collection. While procedures to identify the form of these groups-decomposition inequalities have been previously derived, we substantially enlarge the applicability of the framework. We derive groups-decomposition inequalities subject to weaker independence conditions, with weaker requirements in the configuration of the groups, and additionally allowing for conditioning sets. Furthermore, we show how constraints with higher inferential power may be derived with collections that include hidden variables, and then converted into testable constraints using data processing inequalities. For this purpose, we apply the standard data processing inequality of conditional mutual information and derive an analogous property for a measure of conditional unique information recently introduced to separate redundant, synergistic, and unique contributions to the information that a set of variables has about a target.

Combining Exergy and Pinch Analysis for the Operating Mode Optimization of a Steam Turbine Cogeneration Plant in Wonji-Shoa, Ethiopia.

In this research, the simulation of an existing 31.5 MW steam power plant, providing both electricity for the national grid and hot utility for the related sugar factory, was performed by means of ProSimPlus® v. 3.7.6. The purpose of this study is to analyze the steam turbine operating parameters by means of the exergy concept with a pinch-based technique in order to assess the overall energy performance and losses that occur in the power plant. The combined pinch and exergy analysis (CPEA) initially focuses on the depiction of the hot and cold composite curves (HCCCs) of the steam cycle to evaluate the energy and exergy requirements. Based on the minimal approach temperature difference (∆Tlm) required for effective heat transfer, the exergy loss that raises the heat demand (heat duty) for power generation can be quantitatively assessed. The exergy composite curves focus on the potential for fuel saving throughout the cycle with respect to three possible operating modes and evaluates opportunities for heat pumping in the process. Well-established tools, such as balanced exergy composite curves, are used to visualize exergy losses in each process unit and utility heat exchangers. The outcome of the combined exergy-pinch analysis reveals that energy savings of up to 83.44 MW may be realized by lowering exergy destruction in the cogeneration plant according to the operating scenario.

Forecasting the Expansion of Bactrocera tsuneonis (Miyake) (Diptera: Tephritidae) in China Using the MaxEnt Model.

The invasive pest, Bactrocera tsuneonis (Miyake), has become a significant threat to China's citrus industry. Predicting the area of potentially suitable habitats for B. tsuneonis is essential for optimizing pest control strategies that mitigate its impact on the citrus industry. Here, existing distribution data for B. tsuneonis, as well as current climate data and projections for four future periods (2021-2040, 2041-2060, 2061-2080, and 2081-2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were obtained. The distribution of B. tsuneonis under current and different climate change scenarios in China was predicted using the optimized MaxEnt model, ArcGIS, and the ENMeval data package. Model accuracy was assessed using ROC curves, and the primary environmental factors influencing the distribution of the pest were identified based on the percent contribution. When the regularization multiplier (RM) was set to 1.5 and the feature combination (FC) was set to LQH, a model with lower complexity was obtained. Under these parameter settings, the mean training AUC was 0.9916, and the mean testing AUC was 0.9854, indicating high predictive performance. The most influential environmental variables limiting the distribution of B. tsuneonis were the Precipitation of Warmest Quarter (Bio18) and Temperature Seasonality (standard deviation ×100) (Bio4). Under current climatic conditions, potentially suitable habitat for B. tsuneonis in China covered an area of 215.9 × 104 km2, accounting for 22.49% of the country's land area. Potentially suitable habitat was primarily concentrated in Central China, South China, and East China. However, under future climatic projections, the area of suitable habitat for B. tsuneonis exhibited varying degrees of expansion. Furthermore, the centroid of the total suitable habitat for this pest gradually shifted westward and northward. These findings suggest that B. tsuneonis will spread to northern and western regions of China under future climate changes. The results of our study indicate that climate change will have a major effect on the invasion of B. tsuneonis and have implications for the development of strategies to control the spread of B. tsuneonis in China.