Site Selection Research Articles

The Role of Wind Turbine Siting in Achieving Sustainable Energy Goals

As global energy demands rise, there is an increasing need to transition from fossil fuels, which contribute to environmental harm and have limited reserves, to more sustainable and renewable energy sources. This shift is vital for both protecting the environment and ensuring long-term energy security. Renewable energy, such as wind power, plays a significant role in mitigating climate change and reducing greenhouse gas emissions while also being environmentally harmonious. Wind energy, in particular, is gaining importance as a clean, renewable source of power, with wind turbines serving as key components of this transformation. The success of wind energy projects depends largely on proper site selection. Factors such as wind potential, the topographical structure, environmental sensitivity, and legal considerations must all be carefully analyzed to ensure maximum performance and minimal environmental impact. The site selection process is crucial in optimizing energy production while promoting sustainability. Effective micro-siting strategies, which focus on the specific placement of turbines within a site, are also essential for improving energy efficiency and minimizing environmental disruption. This study highlights the importance of careful site analysis to ensure the successful and sustainable implementation of wind energy projects.

Application of Fuzzy Decision Theory in Multi Objective Logistics Distribution Center Site Selection

Application of Fuzzy Decision Theory in Multi Objective Logistics Distribution Center Site Selection

Municipal solid waste management practices and sanitary landfill site selection using the AHP approach for emerging industrial zones

Selecting the right location for waste management is crucial for emerging and rapidly growing industrial towns. The intricate nature of waste management systems necessitates careful consideration of numerous options and evaluation criteria when selecting a suitable landfill site. This study, conducted in Arerti Town, Ethiopia, utilized the GIS and Analytical Hierarchy Process (AHP) to establish a hierarchical model for identifying a suitable landfill location by considering the ten main influencing factors (groundwater table depth, proximity to boreholes, slope, soil, geology, land use, land cover, proximity to reservoirs and proximity to lineaments, built-up area, and road availability). Besides, GIS were used to manipulate and display spatial data, with all maps graded on a scale of 1 to 5, representing the lowest to highest suitability, respectively. The combined results of the AHP and GIS analyses indicated that 20.6% of the study area was deemed unsuitable, 77.67% moderately suitable and 1.75% highly suitable for a landfill. We chose a 12.5-hectare site in the southwest as the optimal location, taking into account future town expansion and its proximity to urban areas and roads. The findings of this study serve as a valuable resource for decision-makers in assessing waste management challenges in emerging towns and rapidly developing urban areas.

AI-Powered Location Intelligence: Revolutionizing Site Selection and Investment Decisions

This comprehensive article explores the transformative impact of artificial intelligence on location intelligence and site selection in the commercial real estate industry. The article examines how AI-powered systems are revolutionizing traditional approaches to property analysis, investment decisions, and operational efficiency across various real estate sectors. Through an examination of current implementations, benefits, and future trends, this analysis demonstrates how machine learning models, advanced analytics, and real-time data processing capabilities are enabling more accurate predictions, improved decision-making, and enhanced operational performance in the real estate industry. The article highlights the significant advancements in areas such as retail site selection, office space development, and industrial property optimization, while also addressing the quantifiable benefits and return on investment of AI implementation.

Trichoscopic Hallmarks in Non-cicatricial and Cicatricial Alopecia in Indian Population: A Narrative Review

Context: Trichoscopy is a non-invasive, bedside diagnostic tool used to classify alopecia. It is instrumental in distinguishing between non-cicatricial (non-scarring) and cicatricial (scarring) alopecia, aiding in disease classification, prognosis, and treatment decisions. This modality reduces the need for skin biopsies by identifying specific trichoscopic patterns that correlate with disease activity, severity, and potential for hair regrowth, especially in cicatricial alopecia. It also helps select biopsy sites in diagnostically challenging cases. Objectives: The review discusses trichoscopy of various forms of non-scarring and scarring alopecia. Methods: The authors searched the literature on non-cicatricial and cicatricial alopecia and their clinical and trichoscopic characteristics in various research databases. Results: Non scarring alopecia includes androgenetic alopecia (AGA), female pattern hair loss (FPHL), telogen effluvium, alopecia areata (AA), tinea capitis, trichotillomania (TTM), and tractional alopecia. In AGA and FPHL, trichoscopy identifies hair thickness variability and signs like yellow dots and vellus hair, which aid in assessing disease severity and surgical candidacy for hair transplants. Alopecia areata shows unique trichoscopic features like exclamation mark hairs and yellow dots, which help gauge disease activity. Tinea capitis and TTM also display distinct trichoscopic markers, facilitating accurate diagnosis without invasive biopsies. In scarring alopecias such as discoid lupus erythematosus (DLE), lichen planopilaris (LPP), and frontal fibrosing alopecia (FFA), trichoscopy reveals inflammation-related features like Perifollicular hyperkeratosis, erythema, and the absence of follicular ostia, guiding treatment and biopsy decisions. It helps distinguish FFA from conditions like AGA and AA by showing characteristic features like concentric Perifollicular erythema and loss of vellus hair. Morphea and pseudopelade of Brocq each display hallmark trichoscopic features. Morphea, in its early stages, responds well to immunosuppressive treatments, whereas pseudopelade of Brocq, being an end-stage condition, shows limited responsiveness to medical interventions. Conclusions: Overall, this review article emphasizes the importance of trichoscopy as a bedside tool for narrowing differential diagnoses and guiding clinical management in various types of alopecia. It highlights the need for ongoing knowledge of trichoscopic signs to facilitate prompt diagnosis and tailored treatment for both non-scarring and scarring alopecia.

Predictive factors for arteriovenous fistula maturation: A prospective study.

Arteriovenous fistula (AVF) maturation failure remains common despite preoperative ultrasound mapping. Identifying predictive biomarkers can help anticipate primary failure and reducing invasive procedures. Our study aimed to identify clinical and analytical risk factors for primary AVF failure or delay. A prospective study (October 2022-March 2023) included adult patients scheduled for AVF creation. In all patients, a preoperative ultrasound mapping was conducted and AVF maturation assessed at least 6 weeks post-surgery. Clinical, analytical, and demographic data were collected. Eighty patients were included, 62.5% male, and mean age 66.3 years. For distal anastomosis, preoperative vein (3.8 ± 1.2 vs. 2.8 ± 0.6 mm; p 0.002) and supply artery (2.5 ± 0.4 vs. 2.0 ± 0.3 mm; p 0.001) diameters were significant factors impacting primary failure. Also, for proximal anastomosis, the artery diameter (2.4 ± 0.4 vs. 2.0 ± 0.4 mm; p 0.01) had an impact on AVF maturation. ROC curves established for distal AVF a vein diameter cutoff of 3.25 mm (AUC 77.2%) and artery cut-off of 2.35 mm (AUC 74.6%) and for proximal AVF an artery cutoff of 2.25 mm (AUC 76.5%). Distal AVF creation correlated with higher primary failure risk (p < 0.001). No correlation was found between the primary failure rate and the presence of central venous catheter or serum results. In a sub analysis, we found that patients with central venous catheter had higher levels of inflammatory markers. Our study highlights the importance of preoperative evaluation, ultrasound mapping, and careful AVF site selection. Recognizing vein and artery diameter thresholds for optimal outcomes is crucial. Avoiding central venous catheters in suitable patients can positively impact AVF results.

Control of 3′ splice site selection by the yeast splicing factor Fyv6

Pre-mRNA splicing is catalyzed in two steps: 5ʹ splice site (SS) cleavage and exon ligation. A number of proteins transiently associate with spliceosomes to specifically impact these steps (first and second step factors). We recently identified Fyv6 (FAM192A in humans) as a second step factor in Saccharomyces cerevisiae; however, we did not determine how widespread Fyv6’s impact is on the transcriptome. To answer this question, we have used RNA sequencing (RNA-seq) to analyze changes in splicing. These results show that loss of Fyv6 results in activation of non-consensus, branch point (BP) proximal 3ʹ SS transcriptome-wide. To identify the molecular basis of these observations, we determined a high-resolution cryo-electron microscopy (cryo-EM) structure of a yeast product complex spliceosome containing Fyv6 at 2.3 Å. The structure reveals that Fyv6 is the only second step factor that contacts the Prp22 ATPase and that Fyv6 binding is mutually exclusive with that of the first step factor Yju2. We then use this structure to dissect Fyv6 functional domains and interpret results of a genetic screen for fyv6Δ suppressor mutations. The combined transcriptomic, structural, and genetic studies allow us to propose a model in which Yju2/Fyv6 exchange facilitates exon ligation and Fyv6 promotes usage of consensus, BP distal 3ʹ SS.

Protein Modeling with DEER Spectroscopy.

Double electron-electron resonance (DEER) combined with site-directed spin labeling can provide distance distributions between selected protein residues to investigate protein structure and conformational heterogeneity. The utilization of the full quantitative information contained in DEER data requires effective protein and spin label modeling methods. Here, we review the application of DEER data to protein modeling. First, we discuss the significance of spin label modeling for accurate extraction of protein structural information and review the most popular label modeling methods. Next, we review several important aspects of protein modeling with DEER, including site selection, how DEER restraints are applied, common artifacts, and the unique potential of DEER data for modeling structural ensembles and conformational landscapes. Finally, we discuss common applications of protein modeling with DEER data and provide an outlook.

Improving Diversity in a Novel Psoriasis Study

Diverse racial and ethnic representation in clinical trials has been limited, not representative of the US population, and the subject of pending US Food and Drug Administration guidance. Psoriasis presentation and disease burden can vary by skin pigmentation, race and ethnicity, and socioeconomic differences. Overall, there are limited primary data on clinical response, genetics, and quality of life in populations with psoriasis and skin of color (SoC). The Varying Skin Tones in Body and Scalp Psoriasis: Guselkumab Efficacy and Safety trial (VISIBLE) is underway and uses strategies aimed at addressing this persistent gap. To assess the innovative strategies used in the VISIBLE trial to recruit and retain diverse participants in a randomized clinical trial of psoriasis in participants with SoC. This was an ad hoc quality improvement assessment of participant recruitment and retention approaches used by the VISIBLE trial. VISIBLE enrolled and randomized 211 participants (mean [SD] age, 43 [13] years; 75 females [36%] and 136 males [64%]) with SoC and moderate to severe plaque psoriasis from August 2022 to March 2023 to evaluate guselkumab treatment. The self-identified race and ethnicity of the participants was: 1 American Indian/Alaska Native (0.5%), 63 Asian (29.9%), 24 Black (11.4%), 94 Hispanic/Latino (44.5%), 13 Middle Eastern (6.2%), 1 Pacific Islander/Native Hawaiian (0.5%), 12 multiracial (5.7%), and 3 of other race and/or ethnicity (1.4%). Using a combination of objective (colorimetry to determine Fitzpatrick skin type) and self-reported (race and ethnicity consistent with SoC) parameters, VISIBLE sought to broaden inclusion of participants from various backgrounds. Observed improvements were that participant enrollment occurred approximately 7 times faster than anticipated (vs historical recruitment data for psoriasis studies); 211 participants (100%) self-identified themselves as a race or ethnicity other than White; and more than 50% had skin tone in the darker half of the Fitzpatrick skin type spectrum (type IV-VI). Innovations implemented by VISIBLE were (1) assessment of the natural history of postinflammatory pigment alteration and improvements over time using combined objective colorimetry and clinician- and patient-reported outcomes; (2) evaluation of genetic and comorbidity biomarkers relevant to participants with SoC; (3) a diverse demographic-driven approach to site selection (emphasizing investigator and staff diversity and experience with populations with SoC); (4) provision of cultural competency training to enhance participant enrollment and retention; (5) collection of patient-reported outcomes data in participants' primary language; and (6) periodic, blinded central review and feedback on investigator efficacy scoring to promote consistency and accuracy in evaluating psoriasis in participants with SoC. VISIBLE is a unique study focused on addressing important knowledge and data gaps in populations of patients with psoriasis and SoC, with the goal of generating data to help improve clinical care and inform future best practices in diversity within dermatology research. The rapid study enrollment demonstrates that intentional and strategic approaches to clinical trial design and conduct can speed recruitment and bolster participation and retention of diverse populations in a dermatologic setting. ClinicalTrials.gov Identifier: NCT05272150.

Invisible Hand of Sampling for Management: Underlying Needs to Survey a Threatened Seabird Can Bias Aggregated Data

ABSTRACTAimSurveying for a species of concern ahead of proposed activities that alter its habitat is routine practice in conservation and management. Such surveys may accumulate large datasets that could further elucidate trends in abundance and distribution. However, the as‐needed surveying of proposed activities may impart a sample site selection bias on the data if used for another purpose. Management of a threatened, forest‐nesting seabird offered an example of this. Here we assessed how resource management planning and survey requirements can bias clearance monitoring survey data collected prior to proposed timber harvests, if those data are used for other purposes.LocationOregon and Washington, USA.TaxonMarbled Murrelet (Brachyramphus marmoratus).MethodsTo assess how timber planning and other factors influenced marbled murrelet survey location selection, we used logistic regression models to examine habitat associations of marbled murrelet survey sites (n = 9178) encompassing proposed timber harvests, and the survey stations (n = 38,923) therein, across the murrelet's inland range in Washington and Oregon, USA between 1989 and 2021. We then simulated the effect this selective sampling might have on assessments of occupancy trends.ResultsMost habitat characteristics considered did influence where surveys were located, with distance to roads often being the strongest predictor of survey location. The strength of selection for each covariate changed over time, such that a habitat characteristic strongly influenced location selection in a year but was less influential in another year. The simulation analysis suggested that the non‐random selection of survey sites could profoundly bias assessments of occupancy trends.ConclusionsWhen using these clearance monitoring survey data– or any data–beyond their original purpose, careful consideration should be given to the scope of inference provided and analytical methods used, to ensure that observed trends are the product of biological processes and not biased by sampling artefacts.

Regulating the surface chemistry of covalent organic frameworks for enhancement cationic dye removal and identification.

Simultaneous removal and identification of trace-level cationic dye pollutants from water is both important and challenging owing to their highly polar and complex sample matrices. In this study, three covalent organic frameworks (COFs) were synthesized using 2, 4, 6-triformylphloroglucinol with ethidium bromide (EB) containing positively charged groups, 3, 5-diaminobenzoic acid (DABA) containing negatively charged groups, and p-phenylenediamine (Pa) lacking charged groups. These were named EB-COFs, TpPa-1, and DP-COFs, respectively, and were employed as adsorbents for the extraction and identification of cationic dyes. The adsorption performance of the three COFs toward methylene blue (MB) and crystal violet (CV) was investigated. By incorporating carboxyl groups into DP-COFs, the surface chemistry of the adsorbent was effectively tailored, enabling complete exploitation of selective cationic sites. This facilitated dynamic interactions with cationic dyes through multiple adsorption mechanisms, including electrostatic, π-π, and H-bonding interactions. DP-COFs exhibited high adsorption capacities for MB and CV, achieving 383 and 326mgg-1, respectively. The adsorption behavior was further analyzed using adsorption isothermals, kinetics, and thermodynamics. Moreover, DP-COFs were employed as a matrix in laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS) to adsorb and directly identify both cationic dyes without the need for an elution process. This approach demonstrated high sensitivity, high reproducibility, low background interference, and excellent salt tolerance. The limits of detection for MB and CV were 0.12 and 0.04ngmL-1, respectively, representing improvements of 166-fold and 225-fold compared with using DP-COFs solely as a matrix. Recovery rates of both dyes in spiked industrial wastewater and lake water samples ranged from 81.4 to111.1% with RSDs of 1.9-6.3%. These results highlight the high reliability of the proposed method.

Research on Low-Carbon Site Selection Decision Model and Algorithm for Green Logistics

This work proposes a low-carbon site selection decision model and algorithm for green logistics. The focus is to minimize carbon emissions while optimizing the distribution network. With the development of smart shared logistics and increasing environmental awareness, there is an urgent need to tackle issues in urban logistics, including pollution, resource inefficiency, high carbon energy usage, and greenhouse gas emissions. By integrating geographic, environmental, and operational data, the proposed site selection model incorporates multi-objective optimization to balance carbon efficiency with cost-effectiveness in logistics site selection. We employ a hybrid algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to solve the complex decision-making process. The model is tested using real-world logistics data, and the results demonstrate significant reductions in carbon emissions and transportation costs when compared to conventional logistics site selection methods. Further sensitivity analysis reveals that the model is robust under varying transportation demands and geographic constraints. Also, we highlight the role of green logistics in achieving corporate sustainability goals aligning with global carbon neutrality initiatives. We believe this model offers a practical approach to fostering environmentally responsible supply chains and will contribute to the advancement of sustainable logistics operations, helping organizations make informed, eco-friendly decisions in site selection.

Factors Influencing Site Selection for Higher Education Institutes: A Meta-Analysis

Higher education institutions can play an important role in improving society by training productive and committed people through providing knowledge, skills, and necessary values. Studying and conducting research in an optimal location in terms of institute accessibility, resilience against natural and external hazards, and environmental comfort is important for the maximum effectiveness of training. The current study explored which factors contribute to site selection for higher education institutes globally. To do so, a literature review and meta-analysis using secondary data from 1990 to 2024 were conducted to integrate findings from 37 original papers. The results showed that so-called socioeconomic health conditions, student considerations on institute accessibility, and transport services are very important. As such, the socioeconomic health scenarios that affect the improvement of institute location suitability should be given global attention. In addition, we recommend that attention be paid to increasing the level of awareness among individuals about economic features and making necessary interventions for educational infrastructure protection.

A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement

The selection of construction sites for Cultural and Museum Public Buildings (CMPBs) has a profound impact on their future operations and development. To enhance site selection and planning efficiency, we developed a predictive model integrating Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). Taking Shanghai as our case study, we utilized over 1.5 million points of interest data from Amap Visiting Vitality Values (VVVs) from Dianping and Shanghai’s administrative area map. We analyzed and compiled data for 344 sites, each containing 39 infrastructure data sets and one visit vitality data set for the ANN model input. The model was then tested with untrained data to predict VVVs based on the 39 input data sets. We conducted a multi-precision analysis to simulate various scenarios, assessing the model’s applicability at different scales. Combining GA with our approach, we predicted vitality improvements. This method and model can significantly contribute to the early planning, design, development, and operational management of CMPBs in the future.

An innovative hybrid model combining informer and K‐Means clustering methods for invisible multisite solar power estimation

AbstractThe employment of behind‐the‐meter solar photovoltaic (PV) systems has gained increasing popularity in recent years as more individuals and organizations aim to reduce their reliance on conventional grid‐connected power sources and take advantage of the environmental and economic benefits of solar power. However, precisely estimating the potential output of PV systems is a challenging task, since most of the PV systems used in residential properties have been installed behind the meter. Consequently, electric power companies are limited to accessing only the recorded net electricity consumption. This article introduces an innovative approach to estimate behind‐the‐meter PV power generation within a large region, utilizing a limited representative subset. The proposed framework integrates Missforest, that is, a robust tool for missing data imputation, with a hybrid application of K‐Means, Pearson Correlation Coefficient, and Principal Component Analysis, for the precise selection of representative PV sites. Additionally, it leverages the Informer model, a cutting‐edge deep learning‐based time series model, to link the relationship between the PV power generation at representative sites and the total PV power output on the entire region. To conduct a case study, the power output of 367 PV sites and solar radiation measured at 105 weather stations in Taiwan were collected and analyzed. The application of this comprehensive methodology demonstrates a notable advancement in the estimation of “invisible” PV power generation in comparison to other established techniques.

Radioimmunotherapy: a game-changer for advanced non-small cell lung cancer

Lung cancer, particularly non-small cell lung cancer (NSCLC), remains a leading cause of cancer-related deaths, with conventional treatments offering limited effectiveness in advanced stages, due to distant metastases and treatment resistance. Recent advancements in immunotherapy, specifically immune checkpoint inhibitors (ICIs), have shown promise, but their efficacy as standalone therapies are often insufficient. This has led to increased interest in combining ICIs with radiotherapy, known as radioimmunotherapy (iRT), to enhance treatment outcomes. This review explores the mechanisms that underlie the synergy between radiotherapy and immunotherapy. Radiotherapy can induce the “abscopal effect”, eliciting systemic immune responses that reduce tumor burdens outside the treated area. It also increases the expression of major histocompatibility complex class I (MHC-I) on tumor cells, improving immune recognition. Furthermore, radiotherapy can modify the tumor microenvironment by inducing metabolic reprogramming to bolster anti-tumor immunity. We discuss strategies for optimizing iRT, including considerations of radiation doses, fractionation schedules, and treatment site selection, which significantly influence immune responses by enhancing MHC-I expression or promoting T-cell infiltration. Clinical evidence supports the efficacy of iRT in NSCLC and other cancers, though challenges in standardizing treatment protocols and managing side effects persist. Overall, radioimmunotherapy presents a promising approach to improving NSCLC treatment outcomes. Ongoing research into its mechanisms and the refinement of treatment may reshape clinical practice, offering more effective and personalized options for patients with advanced lung cancer. Further studies are essential to validate these findings and optimize therapeutic protocols.

Toward a circular economy in Italian agri-food: upstream partners in insect biorefineries

The insect biorefinery (IB) is central to recycling nutrients from food losses and waste (FLW) in an emerging circular bioeconomy approach. The sustainable leadership of the European insect industry and its integration into the bioeconomy depend on implementing symbiotic production models through site selection and partnerships. Only 8 studies, including 3 in the EU, have examined stakeholder acceptance. This pilot study is the first to focus on potential upstream symbiosis partners (USPs) in the EU, examining the factors that influence the willingness of potential USPs in suitable Italian locations to adopt IB. Preliminary analyses identified a final sample of 31 respondents who completed an online-survey divided into four sections. After data elaboration, a principal component analysis was conducted, considering 27 questions and their correlations with the first two principal components. The analysis revealed that the presence of internal research and development within the company, along with the company’s role in the supply chain and the seasonality of production, had a minor influence on explaining the variance of the data. Interestingly, company size was negatively correlated with the willingness to become part of the insect-rearing supply chain or receive information about this technology. On the contrary, interest in innovative production and self-management of by-products were strongly related. Finally, funding opportunities could positively influence both the Italian and EU contexts, encouraging innovative practices among small and medium-sized enterprises. In contrast, barriers to implementing insect farming include misinformation, anticipated negative media impacts on branding, and resistance to changing by-product management practices. This pilot study serves as a foundational exploration, providing insights into the acceptability of insect farming among potential USPs and offers valuable insights for future studies, thereby shaping the discourse on the acceptability and integration of insect farming within the circular bioeconomy.

Phototactic behavior and oviposition of seven species of Phytoseiidae (Acari: Mesostigmata).

Phototactic behavior and oviposition site selection in phytoseiid predators are crucial for understanding their ecological interactions and optimizing their use in agricultural pest management. This study investigated the phototactic responses and oviposition preferences of seven phytoseiid species of proven or potential importance in biocontrol: Amblydromalus limonicus (Garman & McGregor), Amblyseius herbicolus (Chant), Amblyseius lentiginosus Denmark and Schicha, Neoseiulus barkeri Hughes, Neoseiulus cucumeris (Oudemans), Neoseiulus womersleyi (Schicha), and Phytoseiulus persimilis Athias-Henriot. We hypothesized that these phytoseiid predators use light as a cue, with their phototactic behavior aligning with their respective lifestyles. By presenting adults of these species with two choices (i.e. light and dark) in acrylic arenas, we found that P. persimilis exhibited a significant preference for light. In contrast, other species showed no phototactic preference. The phototactic preferences were similar between genders of all six sexually reproducing species tested in this study. Furthermore, the oviposition site preferences varied significantly among species. Gravid females of As. herbicolus, N. cucumeris, N. womersleyi, and P. persimilis preferred dark sites for egg laying, whereas Ad. limonicus and As. lentiginosus showed no light preference, and N. barkeri preferred light for oviposition. This study highlights the importance of light as an ecological factor influencing phytoseiid behavior and suggests that phototactic and oviposition preferences are adaptations to specific environmental niches. These findings have practical implications for enhancing the effectiveness of phytoseiids in pest management. Further research should investigate the mechanisms driving phototactic responses and light perception in these phytoseiid predators. © 2024 Society of Chemical Industry.

Application of Reactor Technology Assessment for Floating Nuclear Power Plant Site Selection

At ATOMEXPO in 2015, Indonesia expressed its interest in building a Floating Nuclear Power Plant (PLTN). As a technology that is starting to be in demand in various countries, until this paper was written, the only Floating NPP operating commercially in the world is the Akademik Lomonosov from Russia. Indonesia will be included as a newcomer country if it realizes the construction of a Floating NPP. In this regard, the International Atomic Energy Agency (IAEA) published a reactor technology assessment (RTA) guideline. RTA is a method that newcomer countries can use to select a reactor at a predetermined site. This study demonstrated the application of RTA for a different purpose, namely site selection when the reactor has been determined. This is possible because some parameters in RTA are site-specific. This study compared the total score at each site, where the highest total score means the site that needs to be prioritized. The candidate sites used in this study include Gelasa Island, Ujung Lemah Abang (ULA), North Luwu, and Labuhan Badas, and the type of reactor used is KLT-40S, which is used in the Akademik Lomonosov Nuclear Power Plant. The study results found that the site with the highest score was ULA, with a total score of 3.5772, so this site needs to be considered in the construction of the Floating Nuclear Power Plant. Keywords: Nuclear Power Plant (NPP), Floating Nuclear Power Plant, External Hazards, reactor technology assessment (RTA)

Transformers significantly improve splice site prediction

Mutations that affect RNA splicing significantly impact human diversity and disease. Here we present a method using transformers, a type of machine learning model, to detect splicing from raw 45,000-nucleotide sequences. We generate embeddings with residual neural networks and apply hard attention to select splice site candidates, enabling efficient training on long sequences. Our method surpasses the leading tool, SpliceAI, in detecting splice sites in GENCODE and ENSEMBL annotations. Using extensive RNA sequencing data from an Icelandic cohort of 17,848 individuals and the Genotype-Tissue Expression (GTEx) project, our method demonstrates superior performance in detecting splice junctions compared to SpliceAI-10k (PR-AUC = 0.834 vs. PR-AUC = 0.820) and is more effective at identifying disease-related splice variants in ClinVar (PR-AUC = 0.997 vs. PR-AUC = 0.996). These advancements hold promise for improving genetic research and clinical diagnostics, potentially leading to better understanding and treatment of splicing-related diseases.