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Pyosalpinx is the collection of pus in the fallopian tube. Pyosalpinx usually follows pelvic inflammatory disease, sexually transmitted disease, or rarely non-sexually transmitted infection. This is the first-ever report of bilateral pyosalpinx due to intrauterine device in situ for the past 16 years, which presented as appendicitis. Pyosalpinx should be considered in female patients with lower abdominal pain.

In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.

The project aims to develop a sustainable smart irrigation system (SIS) for the indoor plant irrigation by integrating photovoltaic (PV), internet of things (IoT), and rainwater harvesting techniques. The addressed problem involves the inconsistency and tediousness of manual watering, emphasizing the need for a sustainable design for a SIS. The IoT system consists of soil moisture sensor with GSM module powered by PV and an algorithm was developed to adjust irrigation schedules based on soil moisture data. The objectives of this project are to design and optimize the PV-powered irrigation system and implement an Arduino-enabled automatic system with SMS-triggered functionality. The methodology involves system modelling for water requirements and sizing of PV, battery, pump, and MPPT based on the load demand. The rainwater harvesting structure designed ensures water sustainability for plants’ irrigation. The system is then implemented using moisture and ultrasonic sensors managed by Arduino Uno embedded system. The electrical performance of the PV was analyzed on both cloudy and moderately luminous days, with irradiance ranging from 250.4 to 667.8 and 285.5 to 928 W/m 2 , respectively. The average output voltage and current of the battery were observed to be 13.04 V and 0.37 A (cloudy), and 13.45 V and 0.47 A (moderate) days, respectively. The rainwater collection test revealed more than 36 L in the tank after one week, indicating it could sustain watering the three plants for 72 days. Based on the analysis, the project can save 14.97 kgCO 2 emissions per year compared to the current emissions released into the environment. The overall cost of the system is approximately RM670 (US$139.50). The SIS aligns with SDG 7, promoting affordable and integrates with 12 th Malaysia Plan for more efficient and environmentally friendly agricultural and water management practices.

The majority of studies investigating the environmental Kuznets curve predominantly focus on atmospheric indicators, thereby neglecting other environmental indicators such as land, sea, coastal, coral reefs, freshwater, and biodiversity indicators. This study aims to examine the environmental Kuznets curve by using capture fisheries production as a biodiversity indicator. The study uses a panel of 14 countries, of which 10 are newly industrialized and the other 4 are fast-emerging countries. The study applies the CADF and CIPS unit root tests to identify the integration order as proposed by Pesaran ( 2007 ). After identifying the unique order of integration, the Westerlund ( 2007 ) panel cointegration is applied. A long-run relationship is confirmed among the variables. The study revealed that an N-pattern relationship exists between capture fisheries production (CFP) and growth of the economy in the panel of selected countries. The industry focuses on achieving a cleaner environment and promotes the sustainable development of the fisheries. Financial development has a negative and significant effect on CFP. This reflects that domestic credit is not only used for the capture of fish but also for conservation purposes. The exports of goods and services have a positive relationship with CFP, while imports have a negative and significant effect on CFP. Policies to promote investments in the conservation of fisheries should be implemented, and credit creation should be directed by appropriate legislation to ensure the conservation of biodiversity and environmental sustainability.

Animal models of human disease are an invaluable component of studies aimed at understanding disease pathogenesis and therapeutic possibilities. Mutations in the gene encoding retinitis pigmentosa GTPase regulator (RPGR) are the most common cause of X-linked retinitis pigmentosa (XLRP) and are estimated to cause 20% of all retinal dystrophy cases. A majority of RPGR mutations are present in ORF15, the purine-rich terminal exon of the predominant splice-variant expressed in retina. Here we describe the genetic and phenotypic characterization of the retinal degeneration 9 (Rd9) strain of mice, a naturally occurring animal model of XLRP. Rd9 mice were found to carry a 32-base-pair duplication within ORF15 that causes a shift in the reading frame that introduces a premature-stop codon. Rpgr ORF15 transcripts, but not protein, were detected in retinas from Rd9/Y male mice that exhibited retinal pathology, including pigment loss and slowly progressing decrease in outer nuclear layer thickness. The levels of rhodopsin and transducin in rod outer segments were also decreased, and M-cone opsin appeared mislocalized within cone photoreceptors. In addition, electroretinogram (ERG) a- and b-wave amplitudes of both Rd9/Y male and Rd9/Rd9 female mice showed moderate gradual reduction that continued to 24 months of age. The presence of multiple retinal features that correlate with findings in individuals with XLRP identifies Rd9 as a valuable model for use in gaining insight into ORF15-associated disease progression and pathogenesis, as well as accelerating the development and testing of therapeutic strategies for this common form of retinal dystrophy.

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage (MV) distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using MATLAB software R2014b and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three-phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault from other faults in the power system.

Nicholas Katsanis and colleagues report that a common allele of RPGRIP1L is associated with photoreceptor loss in ciliopathies. An A229T variant in RPGRIP1L compromises binding to RPGR and modifies the retinal degeneration phenotype in ciliopathies caused by mutations in other genes. Despite rapid advances in the identification of genes involved in disease, the predictive power of the genotype remains limited, in part owing to poorly understood effects of second-site modifiers. Here we demonstrate that a polymorphic coding variant of RPGRIP1L (retinitis pigmentosa GTPase regulator-interacting protein-1 like), a ciliary gene mutated in Meckel-Gruber (MKS) and Joubert (JBTS) syndromes, is associated with the development of retinal degeneration in individuals with ciliopathies caused by mutations in other genes. As part of our resequencing efforts of the ciliary proteome, we identified several putative loss-of-function RPGRIP1L mutations, including one common variant, A229T. Multiple genetic lines of evidence showed this allele to be associated with photoreceptor loss in ciliopathies. Moreover, we show that RPGRIP1L interacts biochemically with RPGR, loss of which causes retinal degeneration, and that the Thr229-encoded protein significantly compromises this interaction. Our data represent an example of modification of a discrete phenotype of syndromic disease and highlight the importance of a multifaceted approach for the discovery of modifier alleles of intermediate frequency and effect.

Background The integration of generative artificial intelligence (AI) tools into nursing practice has accelerated documentation processes but it has also raised concerns regarding the completeness, accuracy, and clinical safety of AI-generated care plans. Despite the growing use of tools like ChatGPT, Gemini, and PopAI in clinical and academic settings, no validated instrument currently exists to assess the quality of such documentation across the nursing process. Objective This study aimed to develop and validate the Nursing Process Evaluation Tool (NPET), a multidimensional instrument designed to assess the quality of AI-generated nursing documentation within the ADPIE (Assessment, Diagnosis, Planning, Implementation, Evaluation) framework. Methods A two-phase cross-sectional study was conducted. Phase I focused on item development and content validation via two rounds of expert review ( n  = 23). Phase II evaluated the NPET’s psychometric properties by assessing 64 AI-generated nursing care plans based on eight clinical scenarios using eight AI models. A total of 368 individual expert ratings were yielded. Reliability (Cronbach’s α, ICC), content and construct validity (I-CVI, S-CVI/Ave, exploratory factor analysis), and comparative model performance (repeated-measures ANOVA with Tukey post hoc tests) were analyzed. Results The NPET demonstrated strong content validity (S-CVI/Ave = 0.88) and excellent internal consistency (α = 0.85–0.94 across domains). Inter-rater reliability was high (ICC_average = 0.85–0.94). Exploratory factor analysis supported the proposed structure: four domains were unidimensional, while the Assessment domain revealed two interpretable factors. Although the overall ANOVA did not reveal statistically significant differences among AI models (F (7, 360) = 1.57, p  = 0.144, ω² = 0.01), descriptive trends and post hoc tests showed that paid models consistently outperformed free versions. PopAI Paid achieved the highest mean NPET score (M = 3.44 on a 4-point scale), followed by ChatGPT Paid (M = 3.37), while Microsoft Copilot scored the lowest (M = 2.99). The largest pairwise difference—between PopAI Paid and Copilot—yielded a moderate-to-large effect size (Cohen’s d = 0.60). Conclusion The NPET is a valid and reliable tool for evaluating the quality of AI-generated nursing care plans. While the overall ANOVA did not yield statistically significant differences across AI models, the consistently high performance across tools and meaningful differences observed in descriptive and post hoc comparisons support the tool’s utility in nursing education, clinical auditing, and AI benchmarking. Future research should explore its application in real-world documentation and monitor its adaptability to evolving AI technologies.

This study presents a nature-inspired, and metaheuristic-based Marine predator algorithm (MPA) for solving the optimal power flow (OPF) problem. The significant insight of MPA is the widespread foraging strategy called the Levy walk and Brownian movements in ocean predators, including the optimal encounter rate policy in biological interaction among predators and prey which make the method to solve the real-world engineering problems of OPF. The OPF problem has been extensively used in power system operation, planning, and management over a long time. In this work, the MPA is analyzed to solve the single-objective OPF problem considering the fuel cost, real and reactive power loss, voltage deviation, and voltage stability enhancement index as objective functions. The proposed method is tested on IEEE 30-bus test system and the obtained results by the proposed method are compared with recent literature studies. The acquired results demonstrate that the proposed method is quite competitive among the nature-inspired optimization techniques reported in the literature.

This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.