Impact Of Scarcity Research Articles

Distributed photovoltaic power forecasting based on personalized federated adversarial learning

Existing distributed photovoltaic (PV) power forecasting methods fail to address the impact of sample scarcity and heterogeneity in PV power data. Moreover, training a single prediction model proves challenging to meet the personalized forecasting needs of different PV stations in distributed environments. This paper proposes a personalized federated generative adversarial network (PFedGAN)-based DPV power forecasting method. Leveraging the federated learning (FL) framework, it achieves collaborative training of prediction models among DPV stations while preserving data privacy. y introducing generative adversarial networks (GAN) and personalized strategy optimization into the FL training process, it alleviates data scarcity issues and reduces the impact of data heterogeneity. A TimesNet-DeepAR (TNE-DeepAR) power prediction model is designed, where the TimesNet module extracts correlations between PV power data from different time periods, and the DeepAR module facilitates PV power prediction, mitigating the effects of meteorological factors' multi-periodic variations on PV power. Experimental results show that the proposed hybrid prediction model reduces the average mean absolute percentage error (MAPE) by 30–40 % compared to single models. The proposed approach reduces the MAPE by 9.79 % compared to traditional methods and by 49.62 % for PV stations with scarce data.

Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions

Accurate forecasting of solar power output and load demand is critical for the efficient operation and management of isolated microgrids, where reliability and sustainability are paramount. Traditional methods often struggle with data scarcity, limitations in capturing intricate temporal dynamics, and lack of scalability. This research introduces a novel multi-task learning (MTL) model, the Self-Aware Quantized Multi-Task ConvLSTM (SAQ-MTCLSTM), which addresses these challenges by jointly forecasting solar power and load demand while leveraging shared representations across these interdependent time series. The SAQ-MTCLSTM incorporates a sophisticated architecture that combines convolutional and LSTM layers with self-aware quantization to enhance computational efficiency and model adaptability. This allows for effective knowledge transfer, improved data utilization, and the capture of intricate temporal patterns. Evaluated on a real-world isolated microgrid dataset from the Micro Reseau Mafate research project, the model demonstrates significant improvements in forecasting accuracy, with MSE values of 0.0021 for solar power output and 0.0037 for load demand forecasting, compared to single-task and other advanced models. Extensive experiments highlight the impact of data scarcity, seasonality patterns, and microgrid topology on forecasting performance. Such forecasting is essential to optimize the integration and utilization of renewable resources, enhancing operational stability and reducing dependency on external energy supplies.

A pseudo-label supervised graph fusion attention network for drug–target interaction prediction

Drug–target interaction (DTI) prediction can reveal new drug targets and assist in drug repositioning. It can also help identify the most potential candidate drugs for specific targets, advancing new drug discovery. Graph Convolutional Networks (GCNs) have been employed to explore potential relationships between drug–target pairs (DTPs) due to their strong learning capabilities. However, existing methods primarily rely on static graphs constructed from topological structures. These graphs may contain missing or meaningless edges, limiting the ability of GCNs to capture node embeddings. Furthermore, the lack of labels in practical DTI prediction presents a significant challenge. To address these issues, this paper introduces a pseudo-label supervised graph fusion attention network for DTI prediction (PSF-DTI). Specifically, we establish a far-neighbor graph to capture robust differential information between DTPs, compensating for the limitations of traditional topology graphs. Additionally, we create an adaptive graph to dynamically update edge information for more accurate graph structures. During training, we assign pseudo-labels to unlabeled data based on feature similarities between DTPs, mitigating the impact of label scarcity. Comparative experiments with seven state-of-the-art algorithms on public datasets demonstrate the superior performance of PSF-DTI. Extensive ablation experiments validate the effectiveness of the proposed approaches. Our findings suggest that PSF-DTI offers significant advantages in DTI prediction, providing innovative methods and perspectives for future drug discovery and repositioning.

Resource Constraints Lead to Biased Attention but Decrease Unethical Behavior

ABSTRACTSubjective experiences of resource scarcity can make individuals short‐term oriented, capture attention, and trigger feelings of unfairness. However, the impact of scarcity on information processing and ethical decision‐making remains poorly understood. This eye‐tracking study explored how acute financial scarcity affects selective information search and ethical decision‐making in an economic task with competing incentives (N = 60, 12,000 observations). Results revealed that participants experiencing financial scarcity displayed a strong attentional bias towards financially tempting information, although they ultimately did not behave more unethically. These findings might reveal a “moral boundary” dictating when attentional biases translate into decision‐making. Our results contribute to understanding how individuals in scarcity contexts process and prioritize information in ethical decision‐making, helping organizations and policymakers combat stereotypes surrounding resource‐deprived individuals, and design evidence‐based policy interventions promoting ethical behavior in financially scarce situations.

TransTLA: A Transfer Learning Approach with TCN-LSTM-Attention for Household Appliance Sales Forecasting in Small Towns

Deep learning (DL) has been widely applied to forecast the sales volume of household appliances with high accuracy. Unfortunately, in small towns, due to the limited amount of historical sales data, it is difficult to forecast household appliance sales accurately. To overcome the above-mentioned challenge, we propose a novel household appliance sales forecasting algorithm based on transfer learning, temporal convolutional network (TCN), long short-term memory (LSTM), and attention mechanism (called “TransTLA”). Firstly, we combine TCN and LSTM to exploit the spatiotemporal correlation of sales data. Secondly, we utilize the attention mechanism to make full use of the features of sales data. Finally, in order to mitigate the impact of data scarcity and regional differences, a transfer learning technique is used to improve the predictive performance in small towns, with the help of the learning experience from the megacity. The experimental outcomes reveal that the proposed TransTLA model significantly outperforms traditional forecasting methods in predicting small town household appliance sales volumes. Specifically, TransTLA achieves an average mean absolute error (MAE) improvement of 27.60% over LSTM, 9.23% over convolutional neural networks (CNN), and 11.00% over the CNN-LSTM-Attention model across one to four step-ahead predictions. This study addresses the data scarcity problem in small town sales forecasting, helping businesses improve inventory management, enhance customer satisfaction, and contribute to a more efficient supply chain, benefiting the overall economy.

Life cycle assessment of a circular textile value chain: the case of a garment made from chemically recycled cotton

Abstract Purpose The textile industry faces major challenges in reducing environmental impacts along the whole value chain. The overall aim of this paper was to assess the potential environmental benefit of a circular textile value chain, by evaluating a garment partly made from a chemically recycled cellulose carbamate fibre. The cellulose carbamate technology is a novel technology that turns cotton-rich textile waste into a cotton-like regenerated fibre. Methods Life cycle assessment was performed to evaluate the environmental impacts of a garment made from the chemically recycled fibre, considering the whole life cycle. The evaluation also considered that the garment was part of a take-back system, meaning that the garment is collected for recycling after consumer use and thereby helps in closing the loop of the circular textile value chain. The focus of the assessment was on climate impact, water scarcity impact and land use impact. Furthermore, sensitivity analyses were included to test parts of the European Commission’s product environmental footprint method, e.g. the impact of applying the circular footprint formula. Results and discussion The results showed that (1) using a recycled cellulose carbamate fibre over primary conventional cotton showed benefits in all considered environmental impact categories; (2) compared to organic cotton, the benefits were also shown for the land use impact category; (3) the cradle to gate processes were the main hotspots for the garment’s life cycle, meaning that using a recycled feedstock is not the only measure needed to reduce environmental burdens; (4) the use phase, and in particular using the garment to its full life length, is crucial for mitigating the environmental impact per garment use; and (5) methodological choices related to the use of recycled feedstock, and sending materials to recycling at end-of-life, affect the outcome of the study. Conclusions Selecting a chemically recycled cellulose carbamate fibre over primary fibres showed environmental benefits for the evaluated garment, but there are however trade-offs between different environmental impact categories and fibre types. Furthermore, using recycled fibres is one important step in reducing the environmental concerns of garments, but it is important to also make improvements along the whole textile value chain.

Impact of water scarcity on rural livelihood in the drought-prone region: A review of global perspectives

Water scarcity is a challenging task for the vast majority of people in the world, which has occurred due to changes in weather patterns due to less rainfall than usual. According to perceptions, in some parts of the world, water scarcity will be more frequent and intense now. Over 200 published articles and reports from the period 2003–2023 were considered for the measurement of the global impact of water shortages on livelihoods in arid areas. Such a type of review study is designed for a systematic approach. The manuscript illustrated different dimensions of water scarcity impacts by using electronic search engines, including Google, Scopus, Science Direct, Google Scholar, PubMed, ResearchGate, and Web of Science. The study was then distinguished by including the word water scarcity with keywords such as landscape, vulnerability, adjustment, displaced person, health effect, and risk management to inquire about the necessary articles. The study results illustrated that the shortage of water during the drought period increased the misery of affected people. Droughts affect the livelihood of these people in agronomic activities, food production and security, and natural assets, causing prolonged suffering.

A New Dual-Branch Embedded Multivariate Attention Network for Hyperspectral Remote Sensing Classification

With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and producing training samples, the limited training sample is a common problem that researchers often face. Furthermore, efficient algorithms are necessary to excavate the spatial and spectral information from these images, and then, make full use of this information with limited training samples. To solve this problem, a novel two-branch deep learning network model is proposed for extracting hyperspectral remote sensing features in this paper. In this model, one branch focuses on extracting spectral features using multi-scale convolution and a normalization-based attention module, while the other branch captures spatial features through small-scale dilation convolution and Euclidean Similarity Attention. Subsequently, pooling and layering techniques are employed to further extract abstract features after feature fusion. In the experiments conducted on two public datasets, namely, IP and UP, as well as our own labeled dataset, namely, YRE, the proposed DMAN achieves the best classification results, with overall accuracies of 96.74%, 97.4%, and 98.08%, respectively. Compared to the sub-optimal state-of-the-art methods, the overall accuracies are improved by 1.05, 0.42, and 0.51 percentage points, respectively. The advantage of this network structure is particularly evident in unbalanced sample environments. Additionally, we introduce a new strategy based on the RPNet, which utilizes a small number of principal components for feature classification after dimensionality reduction. The results demonstrate its effectiveness in uncovering compressed feature information, with an overall accuracy improvement of 0.68 percentage points. Consequently, our model helps mitigate the impact of data scarcity on model performance, thereby contributing positively to the advancement of hyperspectral remote sensing technology in practical applications.

Enhancing environmental performance in biogas production from wastewater-grown microalgae: A life cycle assessment perspective

The production of biogas from microalgae has gained attention due to their rapid growth, CO2 sequestration, and minimal land use. This study uses life cycle assessment to assess the environmental impacts of biogas production from wastewater-grown microalgae through anaerobic digestion within an optimized microalgae-based system. Using SimaPro® 9 software, 3 scenarios were modeled considering the ReCiPe v1.13 midpoint and endpoint methods for environmental impact assessment in different categories. In the baseline scenario (S1), a hypothetical system for biogas production was considered, consisting of a high rate algal pond (HRAP), a settling, an anaerobic digester, and a biogas upgrading unit. The second scenario (S2) included strategies to enhance biogas yield, namely co-digestion and thermal pre-treatment. The third scenario (S3), besides considering the strategies of S2, proposed the biogas upgrading in the HRAP and the digestate recovery as a biofertilizer. After normalization, human carcinogenic toxicity was the most positively affected category due to water use in the cultivation step, accounted as avoided product. However, this category was also the most negatively affected by the impacts of the digester heating energy. Anaerobic digestion was the most impactful step, constituting on average 60.37% of total impacts. Scenario S3 performed better environmentally, primarily due to the integration of biogas upgrading within the cultivation reactor and digestate use as a biofertilizer. Sensitivity analysis highlighted methane yield's importance, showing potential for an 11.28% reduction in ionizing radiation impacts with a 10% increase. Comparing S3 biogas with natural gas, the resource scarcity impact was reduced sixfold, but the human health impact was 23 times higher in S3.

Exploring the Scarcity of Municipal Resources as Contributors to Illegal Strikes and Protests in the City of Tshwane Metropolitan Municipality, South Africa

The shortage of resources at municipalities are a national problem in so many ways. By lacking resources and maintenance municipalities are unable to provide communities with quality service delivery. This article aims to highlight the impact of scarcity of resources in the City of Tshwane (CoT) Metropolitan Municipality which bring about illegal and unprotected strikes by employees, and protests by the local community. When residents or employees are unhappy with poor municipal services, be it no water or electricity for days, poor service delivery, low employee salaries or better working conditions, people tend to take to the streets and show their discontentment with lack of proper services by local government. Strikes and protests arise for a number of reasons, though principally in response to economic conditions. When employees strike for better wages or residence for proper services, both demonstrations have an impact on the economic welfare of any country. The methodological rigor of this paper is ethnographical in nature and data was collected through literature study. Major contributions is to identify advanced methods of policing through intelligence gathering and enforcement when or before illegal strikes and protests take place. This will assist in eliminating fear from the public, tourists, non-striking employees, law enforcement officers and local business operators, etc. The implications should instill the norm of zero tolerance to violence and intimidation, and punishments are inevitable within the prescripts of the law of the Republic of South Africa. Enforcing the law by ensuring that legal mechanisms are reactive and punitive will hinder anyone to partake in illegal strikes or protests.

Comparing Accuracy of Wildfire Spread Prediction Models under Different Data Deficiency Conditions

Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of geographical and environmental variables. However, unlike the ideal conditions simulated in the laboratory, data gaps often occur in real wildfire scenarios, posing challenges to the accuracy and robustness of predictions. It is necessary to explore the extent to which different missing items affect prediction accuracy, thereby providing rational suggestions for emergency decision-making. In this paper, we tested how different conditions of missing data affect the prediction accuracy of existing wildfire spread models and quantified the corresponding errors. The final experimental results suggest that it is necessary to judge the potential impact of data gaps based on the geographical conditions of the study area appropriately, as there is no significant pattern of behavior yet identified. This study aims to simulate the impact of data scarcity on the accuracy of wildfire spread prediction models in real scenarios, thereby enabling researchers to better understand the priority of different environmental variables for the model and identify the acceptable degree of missing data and the indispensable data attributes. It offers new insights for developing spread prediction models applicable to real-world scenarios and rational assessment of the effectiveness of model outcomes.

Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic features

PurposeTo evaluate the potential of synthetic radiomic data generation in addressing data scarcity in radiomics/radiogenomics models. MethodsThis study was conducted on a retrospectively collected cohort of 386 colorectal cancer patients (n = 2570 lesions) for whom matched contrast-enhanced CT images and gene TP53 mutational status were available. The full cohort data was divided into a training cohort (n = 2055 lesions) and an independent and fixed test set (n = 515 lesions). Differently sized training sets were subsampled from the training cohort to measure the impact of sample size on model performance and assess the added value of synthetic radiomic augmentation at different sizes. Five different tabular synthetic data generation models were used to generate synthetic radiomic data based on “real-world” radiomics data extracted from this cohort. The quality and reproducibility of the generated synthetic radiomic data were assessed. Synthetic radiomics were then combined with “real-world” radiomic training data to evaluate their impact on the predictive model's performance. ResultsA prediction model was generated using only “real-world” radiomic data, revealing the impact of data scarcity in this particular data set through a lack of predictive performance at low training sample numbers (n = 200, 400, 1000 lesions with average AUC = 0.52, 0.53, and 0.56 respectively, compared to 0.64 when using 2055 training lesions). Synthetic tabular data generation models created reproducible synthetic radiomic data with properties highly similar to “real-world” data (for n = 1000 lesions, average Chi-square = 0.932, average basic statistical correlation = 0.844). The integration of synthetic radiomic data consistently enhanced the performance of predictive models trained with small sample size sets (AUC enhanced by 9.6%, 11.3%, and 16.7% for models trained on n_samples = 200, 400, and 1000 lesions, respectively). In contrast, synthetic data generated from randomised/noisy radiomic data failed to enhance predictive performance underlining the requirement of true signal data to do so. ConclusionSynthetic radiomic data, when combined with real radiomics, could enhance the performance of predictive models. Tabular synthetic data generation might help to overcome limitations in medical AI stemming from data scarcity.

Systems analysis for energy assets of Iraq influenced by water scarcity

Water security in arid and semi-arid Middle Eastern climates has been severely impacted by effects of climate change such as reduced precipitation, diminished storage, increased evapotranspiration, and prolonged heat waves. These climate effects are compounded in Iraq, where populations, agriculture, industry, and energy rely heavily on varying transboundary water flows to meet water demands. Iraq’s most profitable sector, energy, is especially threatened by insufficient water supply, which complicates government decision making in energy infrastructure development. The goal of this paper is to develop a scenario-based multi-criteria analysis framework to prioritize infrastructure investments in the context of climate change and scarcity of natural resources. Infrastructure facilities are evaluated against social, economic, climate, and hydrologic criteria across a set of disruptive climatological, economic, and social scenarios to identify robust initiatives and the most and least disruptive scenarios to the system. A particular innovation of this paper is the evaluation of hydrology data derived from satellites in determining water scarcity impact on individual energy facilities. The methods are demonstrated for a critical sector of Iraq’s economy: oil and natural gas. The demonstration includes 13 system-order criteria, 44 oil and gas initiatives within Iraq, and seven risk scenarios. The results include an accounting of the most and least disruptive scenarios to energy sector priorities and scenario-based system orderings to guide stakeholders in investment prioritization.

Assessing Metal Use and Scarcity Impacts of Vehicle Gliders

This study assesses the metal composition of two vehicle gliders, configured with different equipment levels and evaluates the risk of short and long-term metal scarcity. Entropy analysis is also used for insights on secondary metal recovery strategies. Fifty-five metals are evaluated, with gold, copper, bismuth, lead, molybdenum, and certain rare-earth metals (REMs) subject to the largest supply risks. Differences in equipment levels significantly impact the short-term supply risk for specific metals. Entertainment and communications equipment contain significant amounts of REMs, whereas mirrors and electrical infrastructure contain considerable shares of gold, silver and copper. Some metals are concentrated in a few components while some are dispersed across thousands, impacting recycling opportunities. The broad metal demand of the gliders underscores the automotive industry's role in supply risks for its own manufacturing needs and other societal domains. This emphasizes the significance of comprehensively evaluating metal requirements beyond powertrains for informed resource management.

Labor scarcity, technology adoption and innovation: evidence from the cholera pandemics in 19th century France

To analyze the impact of labor scarcity on technology adoption and innovation, this study uses the differential spread of cholera across France in 1832, 1849 and 1854, before the transmission mode of this disease was understood. The results suggest that a larger share of cholera deaths in the population, which can be causally linked to summer temperature levels, had a positive and significant short-run effect on technology adoption and innovation in agriculture but a negative and significant short-run impact on technology adoption in industry. These results can be explained by the positive impact of labor scarcity on human capital formation.

The Water Footprint of Pastoral Dairy Farming: The Effect of Water Footprint Methods, Data Sources and Spatial Scale

The water footprint of pastoral dairy milk production was assessed by analysing water use at 28 irrigated and 60 non-irrigated ‘rain-fed’ pastoral dairy farms in three regions of New Zealand. Two water footprint methods, the WFN-based blue water footprint impact index (WFIIblue) and the Available WAter REmaining (AWARE) water scarcity footprint (WFAWARE), were evaluated using different sets of global or local data sources, different rates of environmental flow requirements, and the regional or catchment scale of the analysis. A majority (~99%) of the consumptive water footprint of a unit of pastoral dairy milk production (L/kg of fat- and protein-corrected milk) was quantified as being associated with green and blue water consumption via evapotranspiration for pasture and feed used at the studied dairy farms. The quantified WFIIblue (-) and WFAWARE (m3 world eq./kg of FPCM) indices ranked in a similar order (from lowest to highest) regarding the water scarcity footprint impact associated with pastoral dairy milk production across the study regions and catchments. However, use of the global or local data sets significantly affected the quantification and comparative rankings of the WFIIblue and WFAWARE values. Compared to the local data sets, using the global data sets resulted in significant under- or overestimation of the WFIIblue and WFAWARE values across the study regions and catchments. A catchment-scale analysis using locally available data sets and calibrated models is recommended to robustly assess water consumption and its associated water scarcity impact due to pastoral dairy milk production in local catchments.

2014 The experience of frailty in the Tanzanian hospital system: A qualitative exploration service user and provider perspectives

Abstract Background Frailty is becoming increasingly prevalent in Low- and Middle-Income Countries (LMICs). However, little is known about the lived experience of older people with frailty in their interactions with the healthcare systems in LMICs. This study aimed to explore the experiences, attitudes and needs of hospitalised older people with frailty in a low-income setting, from the perspective of service users and providers. Methods A purposive sample of older people with frailty recently admitted to hospital, their carers and healthcare providers, were recruited from four hospitals in the Kilimanjaro Region of Tanzania. One-to-one and dyad interviews were conducted in Swahili, based around a structured topic guide. Recordings were transcribed and translated into English in an iterative process aimed at preserving the original meaning through “transliteration”. Transcripts were analysed using a reflexive thematic paradigm. Results Interviews with 43 service users and 15 service providers highlighted the cultural norm of respect and value placed on older people; as such it is considered the role of everyone in society to provide care. However, many working age adults struggle with the competing responsibilities of supporting their parents and their children. Maintaining strong relationships with family and financial preparation were therefore considered critical to healthy ageing. The complex health problems and significant care needs of older people with frailty pose a considerable challenge during hospital admission. On the ward, family often take responsibility for personal care and feeding to reduce the work of healthcare providers. Economic worries and resource scarcity impact every part of the patient journey from struggles in accessing hospital services, to quality of care during admission and after discharge. Conclusion Frailty is understood as a communitywide issue in Tanzania, and family are expected to take a much larger role in care during acute hospital admission, partly mitigating the impact of resource scarcity.

Generosity in poverty: The impact of material scarcity from a self-construction perspective

Generosity in poverty: The impact of material scarcity from a self-construction perspective

Supervised Contrastive Learning Framework and Hardware Implementation of Learned ResNet for Real-time Respiratory Sound Classification.

This paper presents a supervised contrastive learning (SCL) framework for respiratory sound classification and the hardware implementation of learned ResNet on field programmable gate array (FPGA) for real-time monitoring. At the algorithmic level, multiple techniques such as features augmentation and MixUp are combined holistically to mitigate the impact of data scarcity and imbalanced classes in the training dataset. Bayesian optimization further enhances the classification accuracy through parameter tuning in pre-processing and SCL. The proposed framework achieves 0.8725 total score (including runtime score) on a ResNet-18 model in both event and record multi-class classification tasks using the SJTU Paediatric Respiratory Sound Database (SPRSound). In addition, algorithm-hardware co-optimizations including Quantization-Aware Training (QAT), merge of network layers, optimization of memory size and number of parallel threads are performed for hardware implementation on FPGA. This approach reduces 40% model size and 70% computation latency. The learned ResNet is implemented on a Xilinx Zynq ZCU102 FPGA with 16ms latency and less than 2% inference score degradation compared to the software model.

Environmental sustainability and cost performances of construction and demolition waste management scenarios: A case study of timber and concrete houses in Thailand

Circular economy (CE) is becoming a vital strategy to tackle two major environmental challenges of building and construction sector, viz., intensive resource consumption and massive construction and demolition waste (CDW) generation. The study assessed six circular economy scenarios for CDW management of concrete and timber houses in Thailand compared to the base case (landfill). Life cycle assessment and life cycle costing were conducted to illustrate the influence of CE strategies on environmental sustainability and cost performance. Material Circularity Indicator (MCI) was evaluated to determine the circularity performances of material use for the CE scenarios. The 100% recycling scenario resulted in 124% and 166–169% reductions of the mineral resource scarcity impact as compared to the conventional CDW management for the concrete and timber houses, respectively. For the concrete house, 100% recycling scenario resulted in the lowest life cycle costs (when combining both internal cost and externalities). On the other hand, the 100% recycling scenario of timber house would bring about the highest cost due to the energy cost. However, for the timber house, energy recovery scenario provided the lowest impact values on global warming, ozone formation, and terrestrial acidification. The internal costs of implementing the CE measures are higher for timber than for concrete. The MCI value for 100% recycling scenario was about 0.55; however, it can be increased to 0.73 (timber house) and 0.75 (concrete house) if the utility lifetime of building materials were extended. The results show the integrating the CE for CDW management can bring benefit to both environmental sustainability and cost performance.