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Unsupervised anomaly detection holds significant importance in large-scale industrial manufacturing. Recent methods have capitalized on the benefits of employing a classifier pretrained on natural images to extract representative features from specific layers, which are subsequently processed using various techniques. Notably, memory bank-based methods, which have demonstrated exceptional accuracy, often incur a trade-off in terms of latency, posing a challenge in real-time industrial applications where prompt anomaly detection and response are crucial. Indeed, alternative approaches such as knowledge distillation and normalized flow have demonstrated promising performance in unsupervised anomaly detection while maintaining low latency. In this paper, we aim to revisit the concept of knowledge distillation in the context of unsupervised anomaly detection, emphasizing the significance of feature selection. By employing distinctive features and leveraging different models, we intend to highlight the importance of carefully selecting and utilizing relevant features specifically tailored for the task of anomaly detection. This article presents a novel approach for anomaly detection, which employs dual model knowledge distillation and incorporates various types of semantic information by leveraging high and low-level semantic information. [ABSTRACT FROM AUTHOR]

Nowadays there is growing demand for surveillance applications due to the safety and security from anomalous events. An anomaly in the video is referred to as an event that has some unusual behavior. Although time is required for the recognition of these anomalous events, computerized methods might help to decrease it and perform efficient prediction. However, accurate anomaly detection is still a challenge due to complex background, illumination, variations, and occlusion. To handle these challenges a method is proposed for a vision transformer convolutional recurrent neural network named ViT-CNN-RCNN model for the classification of suspicious activities based on frames and videos. The proposed pre-trained ViT-base-patch16-224-in21k model contains 224 × 224 × 3 video frames as input and converts into a 16 × 16 patch size. The ViT-base-patch16-224-in21k has a patch embedding layer, ViT encoder, and ViT transformer layer having 11 blocks, layer-norm, and ViT pooler. The ViT model is trained on selected learning parameters such as 20 training epochs, and 10 batch-size to categorize the input frames into thirteen different classes such as robbery, fighting, shooting, stealing, shoplifting, Arrest, Arson, Abuse, exploiting, Road Accident, Burglary, and Vandalism. The CNN-RNN sequential model is designed to process sequential data, that contains an input layer, GRU layer, GRU-1 Layer and Dense Layer. This model is trained on optimal hyperparameters such as 32 video frame sizes, 30 training epochs, and 16 batch-size for classification into corresponding class labels. The proposed model is evaluated on UNI-crime and UCF-crime datasets. The experimental outcomes conclude that the proposed approach better performed as compared to recently published works. [ABSTRACT FROM AUTHOR]

Background: Crossed fused renal ectopia (CFRE) is a common congenital anomaly where one kidney is positioned abnormally on the opposite side of the midline, often fused with the other kidney. However, single ureter draining crossed fused renal ectopia is a rare occurrence. Case report: Here, we report a case of crossed fused renal ectopia with a single ureter in a 46-year-old Nepali male who presented with history of lithuria. Computed tomography revealed that the left kidney was situated on the right side and fused with the right kidney. The renal pelvises of both kidneys were fused, and a single ureter, located on the right side, was draining both kidneys into the bladder. The patient was advised to have regular follow-ups. Conclusion: Crossed fused renal ectopia with a single ureter represents a rare renal anomaly. Asymptomatic patients can typically be managed conservatively. Regular follow-up is recommended to monitor renal function, calculus formation, infections, and malignant changes. [ABSTRACT FROM AUTHOR]

Changes in the composition of various chemical elements in ancient nephrite artifacts due to prolonged burial are critical factors that should not be underestimated. However, the increasingly stringent heritage management has made many techniques impractical. Consequently, portable X‐ray fluorescence (pXRF) has become an indispensable nondestructive field analysis method. This paper aims to make use of the pXRF dataset to distinguish which elements tested are endogenous or exogenous, as well as to gauge which elements have been affected by stronger burial effects. More specifically, we carry out this work on the example of 103 pieces of nephrite excavated from different cemeteries in the Nanyang area. In addition to traditional statistical techniques, a new tool, the concentration‐number (C‐N) fractal method can shed new light on the analysis of the distribution patterns of different elements in excavated nephrite. The anomalous boundaries generated by the method have clear geochemical significance and can be delineated between background zone and disturbed regions. The degree of resistance of different elements to fluctuations in external factors was assessed, which has a direct relationship with the content of the buried soil. Considering the richness of the model, it has the potential to be used in archaeometrics. [ABSTRACT FROM AUTHOR]

Regulator-required public disclosures of net short positions do not provide a profitable investment signal for UK stocks across a variety of portfolio formation methodologies. While long-short (zero initial outlay) portfolios based on this signal usually make a profit on average, it is rarely statistically significant in either gross or risk-adjusted terms. The issue is that the short sides of the portfolios make substantial losses. Unit initial outlay portfolios based on the disclosures do not generally significantly outperform the market, either. Where they do significantly outperform the market, this outperformance is economically modest. [ABSTRACT FROM AUTHOR]

Objective: To examine the clinical characteristics and auditory performance of patients with CHARGE syndrome following cochlear implantation (CI), as well as the prognostic factors affecting auditory outcomes. Study Design: Retrospective cohort. Setting: Tertiary academic center. Methods: A retrospective chart review was performed in patients with CHARGE syndrome who underwent CI from 2007 to 2022. The category of auditory performance (CAP) score was used to assess the CI outcomes, and factors that may affect the speech outcomes were also evaluated. Results: In 14 children with CHARGE syndrome, 22 CIs were performed, 6 unilaterally and 8 bilaterally. The mean age at CI was 25.9 months (range: 10‐62). All patients had ear abnormalities and developmental delays, and cochlear nerve deficiency (CND) was present in all ears. At the last follow‐up (mean: 49.6 months), the mean CAP score improved significantly compared to the preoperative measure (from 0.36 ± 0.81 to 3.21 ± 1.70, P =.001), with 6 patients (42.9%) achieving a CAP score of 4 points or higher. However, between the unilateral and bilateral CI groups, the final CAP score or change in CAP score was similar. Factors including age, coloboma, and CND did not significantly affect speech outcomes (all P >.05). Conclusion: Even though CHARGE syndrome features challenging anomalies, CI can be conducted safely and can offer effective contribution to significant speech improvement. Patients with CHARGE syndrome should be given the opportunity to undergo CI to maximize their audiological progress. [ABSTRACT FROM AUTHOR]

Background: Testicular descent is a physiological process regulated by many factors. Eventually, disturbances in the embryological/fetal development path facilitate the occurrence of scrotal hernia, a congenital malformation characterized by the presence of intestinal portions within the scrotal sac due to the abnormal expansion of the inguinal ring. In pigs, some genes have been related to this anomaly, but the genetic mechanisms involved remain unclear. This study aimed to investigate the expression profile of a set of genes potentially involved with the manifestation of scrotal hernia in the inguinal ring tissue. Methods and results: Tissue samples from the inguinal ring/canal of normal and scrotal hernia-affected male pigs with approximately 30 days of age were used. Relative expression analysis was performed using qPCR to confirm the expression profile of 17 candidate genes previously identified in an RNA-Seq study. Among them, the Myosin heavy chain 1 (MYH1), Desmin (DES), and Troponin 1 (TNNI1) genes were differentially expressed between groups and had reduced levels of expression in the affected animals. These genes encode proteins involved in the formation of muscle tissue, which seems to be important for increasing the resistance of the inguinal ring to the abdominal pressure, which is essential to avoid the occurrence of scrotal hernia. Conclusions: The downregulation of muscular candidate genes in the inguinal tissue clarifies the genetic mechanisms involved with this anomaly in its primary site, providing useful information for developing strategies to control this malformation in pigs and other mammals. [ABSTRACT FROM AUTHOR]

This study investigated the extent to which the low-risk anomaly is present in a pooled sample of nine African frontier equity markets from March 2004 to July 2023. Portfolio-level analysis was used to investigate the total stock returns of equally weighted quintile portfolios sorted on historical volatility. The presence of a monotonic relationship between historical volatility and subsequent returns was evaluated using monotonicity tests, controlling for possible mediating variables. The results revealed evidence of a statistically significant low-volatility premium, except during bull markets. These findings imply that investors can expect to earn higher returns by allocating to stocks with lower historical volatility, but only during bear markets. These results hold for realistic portfolio construction constraints, such as (il)liquidity and high transaction costs. [ABSTRACT FROM AUTHOR]

The decarbonization of the electricity grid is one of the actions that can help reduce fossil fuel emissions, and thus their impact on global warming in the future. This decarbonization will be achieved mainly through the integration and widespread diffusion of renewable power sources. This is also going to be supported by the shift from the paradigm of production–transmission–distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will very likely reduce the reliability of the grid, unless appropriate countermeasures are taken/implemented, hopefully at the same time that the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might possibly affect some of the central renewable (wind, PV, hydrogen) and ancillary technologies that will be used to establish future renewable-based power systems. Accordingly, this paper presents a literature survey, also extending the focus to related datasets that can be used for deeper investigation. It is highlighted that the gaps mainly refer to a lack of a common taxonomy that prevents the establishment of structured knowledge in the scope of renewable-based power systems, a lack of contributions to anomalies/faults specific to wind turbines, and a lack of datasets related to electrolyzers, fuel cells, DC/x conversion, and monitoring and communication systems. Further, in the case of monitoring and communication systems, the scientific literature is both very dated, therefore not considering possible new aspects that would be currently worthy of investigation, and not oriented toward the particular domain addressed, thus considering peculiar aspects that are left out. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the postoperative renal function in persistent cloaca (PC) patients who underwent posterior sagittal anorecto-urethro-vaginopalsty (PSARUVP) and factors influencing the renal functional outcomes. Methods: A questionnaire survey was distributed to 244 university and children's hospitals across Japan. Of the 169 patients underwent PSARUVP, 103 patients were enrolled in the present study. Exclusion criteria was patients without data of renal prognosis. Results: The present study showed that renal anomalies (p = 0.09), vesicoureteral reflux (VUR) (p = 0.01), and hydrocolpos (p = 0.07) were potential factors influencing a decline in the renal function. Approximately half of the patients had a normal kidney function, but 45.6% had a reduced renal function (Stage ≥ 2 chronic kidney disease: CKD). The incidence of VUR was significantly higher in the renal function decline (RFD) group than those in the preservation (RFP) group (p = 0.01). Vesicostomy was significantly more frequent in the RFD group than in the RFP group (p = 0.04). Urinary tract infections (p < 0.01) and bladder dysfunction (p = 0.04) were significantly more common in patients with VUR than in patients without VUR. There was no association between the VUR status and the bowel function. Conclusions: Prompt assessment and treatment of VUR along with bladder management may minimize the decline in the renal function. [ABSTRACT FROM AUTHOR]

The rapid proliferation of the Internet of Things (IoT) has led to a significant surge in interconnected devices across diverse domains, ranging from smart homes and healthcare systems to industrial automation and smart cities. However, this exponential growth has exposed IoT devices to a plethora of cyber threats, including illegal access, data breaches, and malicious attacks, primarily due to their inherent limitations in terms of network capabilities, computational power, and memory. To combat these security challenges and ensure the safety of IoT ecosystems, the development of effective intrusion detection systems has become imperative. Such systems play a crucial role in detecting and preventing unauthorized activities within IoT networks. In this context, this article presents a pioneering approach called DeepLG SecNet, which leverages a combination of deep learning techniques, including Long Short-Term Memory (LSTM), gated Secure Network (SecNet), and Crossover Chaos Game Optimization (CCGO), to fortify IoT devices against unauthorized access and potential threats. To validate the efficacy of the proposed DeepLG SecNet method, various samples were collected from the BoT-IoT dataset and the NSL-KDD dataset. Performance evaluation was conducted using essential metrics to assess the model's detection capabilities in an IoT intrusion context. The experimental analysis yielded promising results, highlighting the effectiveness of the DeepLG SecNet method in intrusion detection for IoT environments. Specifically, DeepLG SecNet outperformed existing methods, demonstrating higher accuracy, precision, recall, and F1 score in safeguarding IoT systems from potential security breaches. [ABSTRACT FROM AUTHOR]

Copyright of Acta Zoologica Lilloana is the property of Fundacion Miguel Lillo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A large amount of thermal energy is stored in the oceans between the tropics, available for conversion into electrical energy using OTEC technology. The aim of this study was to determine the annual and seasonal variability of the oceanic thermal resource in Mexico. Using the WOA18 database, we mapped surface temperature at a 10 m depth, deep cold water (<5 °C), vertical temperature difference (18 and 20 °C), and temperature anomalies. From the results, four areas were analyzed as being suitable for the installation of OTEC technology: Pacific (A), Los Cabos (B), Caribbean (C), and Gulf of Mexico (G). The optimal thermal resource (≥20 °C) was found between a 400 and 1000 m depth in all seasons in A and C, in spring, summer, and autumn in G, and only in summer and autumn in B. The suboptimal thermal resource (between 18 and 20 °C) was present between 400 and 800 m in all seasons in A, C, and G, and in summer and autumn in B. These results provide new information of utmost importance for future location and design considerations of OTEC plants on Mexican coasts, and the methodology can be used in other areas where there is a lack of field data and the development of OTEC technology is being considered. [ABSTRACT FROM AUTHOR]

BACKGROUND: Lumbosacral transitional vertebrae (LSTVs) are congenital anomalies that occur in the spinal segments of L5-S1. These vertebrae result from sacralization of the lowermost lumbar segment or lumbarization of the uppermost sacral segment. When the lowest lumbar vertebra fuses or forms a false joint with the sacrum (pseudoarticulation), it can cause pain and manifest clinically as Bertolotti syndrome. OBSERVATIONS: A 36-year-old female presented with severe right-sided low-back pain. Computed tomography was unremarkable except for a right-sided Castellvi type IIA LSTV. The pain proved refractory to physical therapy and lumbar epidural spinal injections, but targeted steroid and bupivacaine injection of the pseudoarticulation led to 2 weeks of complete pain relief. She subsequently underwent minimally invasive resection of the pseudoarticulation, with immediate improvement in her low-back pain. The patient continued to be pain free at the 3-year follow-up. LESSONS: LSTVs alter the biomechanics of the lumbosacral spine, which can lead to medically refractory mechanical pain requiring surgical intervention. Select patients with Bertolotti syndrome can benefit from operative management, including resection, fusion, or decompression of the pathologic joint.

Abstract Background Crossed fused renal ectopia (CFRE) is a common congenital anomaly where one kidney is positioned abnormally on the opposite side of the midline, often fused with the other kidney. However, single ureter draining crossed fused renal ectopia is a rare occurrence. Case report Here, we report a case of crossed fused renal ectopia with a single ureter in a 46-year-old Nepali male who presented with history of lithuria. Computed tomography revealed that the left kidney was situated on the right side and fused with the right kidney. The renal pelvises of both kidneys were fused, and a single ureter, located on the right side, was draining both kidneys into the bladder. The patient was advised to have regular follow-ups. Conclusion Crossed fused renal ectopia with a single ureter represents a rare renal anomaly. Asymptomatic patients can typically be managed conservatively. Regular follow-up is recommended to monitor renal function, calculus formation, infections, and malignant changes.

Predicting abnormalities of wearable medical devices plays a very important role in saving the lives and health of patients. This importance has opened new horizons for researchers with the development of newer algorithms. The long-term memory algorithm (LSTM) is one of the most important methods that are a special type of recurrent neural network (RNN) that has a high ability in this field and greatly increases the accuracy of correct and incorrect prediction of these abnormalities. In the current research, by using this algorithm and taking into account different parameters, the anomalies related to the sensors of the research field were determined. The results showed that there are influential parameters in the construction of this architecture, which include 3 very important factors: the number of neurons in the LSTM layer, the batch size, and the activation function. Also, the LSTM architecture together with the Dropout layer, with parameters Batch size = N = 128 and Tanh activation function shows a better performance and the lowest amount of error (MAPE) as well as the amount of the calculated mean square error (RMSE) in determining the anomaly. have sensors in the medical field. Investigations related to the results of 16 repetitions of optimization also showed that the process of reducing errors in the correct and incorrect identification of anomalies in the training phase has reached its lowest level with the increase in the number of tests, which shows the optimality and appropriateness of the work process. Therefore, this algorithm has a very good ability to identify errors in sensors and medical wearables, and it will be of great help in identifying the possible failure of sensors, and critical conditions of the patient, informing and finally helping patients in time. [ABSTRACT FROM AUTHOR]

Unsupervised anomaly detection aims to construct a model that effectively detects invisible anomalies by training and reconstruct normal data. While a significant amount of reconstruction-based methods has made effective progress for time series anomaly detection, challenges still exist in aspects such as temporal feature extraction and generalization ability. Firstly, temporal features of data are subject to local information interference in reconstruction methods, which limits the long-term signal reconstruction methods. Secondly, the training dataset collector is subject to information nourishment such as collection methods, collection periods and locations, and data patterns are diverse, requiring the model to rebuild normal data according to different patterns. These issues hinder the anomaly detection capability of reconstruction-based methods. We propose an unsupervised anomaly detection model based on a diffusion model, which learns normal data pattern learning through noisy forward diffusion and reverse noise regression. By using a cascaded structure and combining it with a structured state space layer, long-term time series signal feature can be well extracted. Different collection signals are distinguished by introducing collector entity ID embedding. The method proposed in this article significantly improves performance in experimental tests on three public datasets. Innovative aspects: (1) Utilizing the S4 method to capture long-term dependencies; (2) Employing a diffusion model for reconstruction learning; (3) Leveraging embedding techniques to enhance different pattern learning. [ABSTRACT FROM AUTHOR]

The purpose of this research was to use a historical method and core principles from scientific philosophy to explain why mistakes were made in the development of the lactic acidosis construct. On a broader scope, this research explains what science is, why some scientists despite good intention, often get it wrong, and why it takes so long (decades) to correct these errors. Science is a human behaviour that consists of the identification of a problem based on the correct application of prior knowledge, the development of a method to best resolve or test the problem, completion of these methods to acquire results, and then a correct interpretation of the results. If these steps are done correctly there is an increased probability (no guarantee) that the outcome is likely to be correct. Thomas Kuhn proposed that you can understand what science is from how it has been performed, and from his essays he revealed a very dysfunctional form of science that he called ‘normal’ (due the preponderance of its presence) science. Conversely, Karl Popper was adamant that the practice of ‘normal’ science revealed numerous flaws that deviate from fundamental principles that makes science, science. Collectively, the evidence reveals that within the sports medicine and health sciences, as with all disciplines, errors in science are more frequent than you might expect. There is an urgent need to improve how we educate and train scientists to prevent the pursuit of ‘normal’ science and the harm it imparts on humanity.

INTRODUCTION: The aim of this study is to examine the diagnosis types and rates of patients with central nervous system (CNS) anomalies detected in the prenatal period between 2021-2022 in our perinatology clinic and to contribute to the literature. METHODS: Our study included 191 patients with CNS anomaly and whose pregnancy results were reached. Demographic characteristics of the patients, additional detected anomalies, fetal Magnetic Resonance Imaging (MRI) and genetic results, obstetric and neonatal results were evaluated if requested. RESULTS: Neural tube defects (NTD) were found most frequently at 75.3%, and among these, acrania was found most frequently at 27.2%. While 74.3% of the detected CNS anomalies were isolated anomalies, it was found that 5.2% were accompanied by additional CNS anomalies and 21.9% were accompanied by additional extracranial anomalies. Abnormal karyotype was obtained in 12.1% of the patients who wanted to have a prenatal diagnosis test. It was found that 11% of the patients requested fetal MRI and ventriculomegaly was the most common fetal MRI indication. While the pregnancies of 56.5% of the patients were terminated, 37.7% of them gave live births. The rate of surgical intervention in live-born babies with CNS anomalies was found to be 56.9%. A total mortality rate of 43% was found in live-born babies. DISCUSSION AND CONCLUSION: Since CNS anomalies are associated with serious morbidity and mortality; Prenatal diagnosis is very important so that families can be offered a pregnancy termination option and those who will continue to be pregnant should be informed about the treatment and rehabilitation processes of their babies.

Bilobed or duplicate gallbladders are the rare anomalies of the biliary system occurring between the 5th and 6th weeks of embryonic life, of which Type 2 ductular variant is the commonest. Patients may be asymptomatic or symptomatic, with majority of the cases being detected intraoperatively or during re-operation. Once suspected a detailed preoperative evaluation is a must to avoid both intraoperative and postoperative complications. We report a case of a 34-year-old female who presented with acute onset of abdominal pain and was detected with symptomatic bilobed gallbladder (H Type) with cholelithiasis, with a resolution of symptoms after a successful laparoscopic cholecystectomy. The case emphasizes the importance of detailed preoperative evaluation in the form of ultrasound and magnetic resonance cholangiopancreatography to be considered during the management of biliary pathologies to avoid any intra- and post-operative complications.

Aim: This study aimed to evaluate the pregnancy outcomes of patients who applied to our clinic between the 11th and 14th weeks of pregnancy and whose nuchal translucency (NT) measurement was ≥1.5 multiples of the median (MoM). Material and Methods: The study included 85 patients whose NT measurement was determined ≥1.5 MoM and pregnancy results were available. Demographic characteristics of the patients, prenatal invasive diagnostic test results, fetal anomaly screening, fetal echocardiography (ECHO) results, and neonatal and obstetric results were evaluated. Results: Abnormal karyotype was detected in 10.6% (n=9) of the patients. Trisomy 21 was the most common chromosomal anomaly. Fetal structural anomaly was detected in 29.4% (n=25) of the patients. A structural fetal anomaly was detected in 21% (n=13) of fetuses with normal karyotypes and 66.7% (n=6) of fetuses with abnormal karyotypes. Cardiac anomalies were found to be the most common anomalies with 9.7% (n=6) in patients with normal karyotype. NT and NT MoM values in patients with fetal structural (both p=0.001) or chromosomal anomalies (p=0.011, and p=0.019, respectively) were found significantly higher than those without. NT and NT MoM values in patients whose pregnancies resulted in fetal loss were found significantly higher than in patients who had a live birth (both p=0.001). Conclusion: Increasing NT or NT MoM values indicate an increase in the risk of chromosomal anomalies, structural anomalies, and poor pregnancy outcomes in the fetus. Fetal anomaly screening and fetal ECHO should be recommended in patients with increased NT, even if a normal karyotype is detected.

We present a case of an 18-year-old female referred for an MRI pelvis to evaluate suspected uterine anomaly by ultrasound. The MRI showed a complete septate uterus and in addition, an elongated tubular structure (isointense to the spleen) extending from the left ovary in the left retroperitoneal region/left paracolic gutter to the under-splenic surface. CT abdomen and pelvis revealed this to be a similarly enhancing structure as the spleen and appears as a tubular retroperitoneal structure connecting the left ovary to the spleen with associated vasculature joining the splenic vein cranially and the ovarian vessels caudally consistent with splenogonadal fusion.

The Northeastern China cold vortex (NCCV) is one type of strong cyclonic vortex that occurs near Northeastern China (NEC), and NCCV activities are typically accompanied by a series of hazardous weather. This paper employed an automatic algorithm to identify the NCCVs from 1979 to 2018 and analyzed their circulation patterns and climatic impacts by using the defined NCCV intensity index (NCCVI). The analysis revealed that the NCCV activities in summer exhibited a strong inter-annual variability, with an obvious periodicity of 3-4 years and 6-7 years, but without significant trends. In years when the NCCVI was high, NEC experienced negative geopotential height anomalies, cyclonic circulation, and cooler temperature anomalies, which were conducive to the maintenance and development of NCCV activities. Furthermore, large amounts of water vapor converged in NEC through two transportation routes as the NCCVs intensified, leading to a significant positive (negative) correlation with the summer precipitation (surface temperature) in NEC. The Atlantic sea surface temperature (SST) anomalies were closely related to summer NCCV activities. As the Atlantic SST rose, large amounts of surface sensible and latent heat flux were transported into the lower troposphere, inducing a positive geopotential height anomaly that occurred on the east side of the heat source. As a result, an eastward diverging flow was formed in the upper troposphere and propagated downstream, i.e., the eastward propagating Rossby wave train, which eventually led to a coupled circulation in the Ural Mountains and NEC, as well as more intensive NCCV activities in summer. [ABSTRACT FROM AUTHOR]

The purpose of this research was to use a historical method and core principles from scientific philosophy to explain why mistakes were made in the development of the lactic acidosis construct. On a broader scope, this research explains what science is, why some scientists despite good intention, often get it wrong, and why it takes so long (decades) to correct these errors. Science is a human behaviour that consists of the identification of a problem based on the correct application of prior knowledge, the development of a method to best resolve or test the problem, completion of these methods to acquire results, and then a correct interpretation of the results. If these steps are done correctly there is an increased probability (no guarantee) that the outcome is likely to be correct. Thomas Kuhn proposed that you can understand what science is from how it has been performed, and from his essays he revealed a very dysfunctional form of science that he called 'normal' (due the preponderance of its presence) science. Conversely, Karl Popper was adamant that the practice of 'normal' science revealed numerous flaws that deviate from fundamental principles that makes science, science. Collectively, the evidence reveals that within the sports medicine and health sciences, as with all disciplines, errors in science are more frequent than you might expect. There is an urgent need to improve how we educate and train scientists to prevent the pursuit of 'normal' science and the harm it imparts on humanity. [ABSTRACT FROM AUTHOR]

Afyonkarahisar and its surroundings are in the middle of the Akşehir-Simav Fault System. Before the earthquakes in the region, radon anomalies were observed in some well waters. Linear equations between radon minima and earthquake magnitudes (Mw) were obtained, and correlation coefficients (R2) were calculated. The correlation coefficient (R2) values of the wells from 1 to 6 were obtained as 0.93, 0.83, 0.82, 0.97, 0.87 and 0.85, respectively. Good correlations (high R2 values) were obtained between the radon minimum and the earthquake magnitude (Mw) in 6 wells. The precursory minimum observed at the radon concentration in groundwater decreased as the earthquake's magnitude increased. [ABSTRACT FROM AUTHOR]

Afyonkarahisar and its surroundings are in the middle of the Akşehir-Simav Fault System. Before the earthquakes in the region, radon anomalies were observed in some well waters. Linear equations between radon minima and earthquake magnitudes (Mw) were obtained, and correlation coefficients (R2) were calculated. The correlation coefficient (R2) values of the wells from 1 to 6 were obtained as 0.93, 0.83, 0.82, 0.97, 0.87 and 0.85, respectively. Good correlations (high R2 values) were obtained between the radon minimum and the earthquake magnitude (Mw) in 6 wells. The precursory minimum observed at the radon concentration in groundwater decreased as the earthquake's magnitude increased. [ABSTRACT FROM AUTHOR]

The calculation of satellite orbit involves some very complex formula derivations and expansions, which are very difficult to manually derive and prone to errors. And the efficiency of manual derivation is not high. We can use computer algebra systems to derive complex formulas related to satellite orbits. This can avoid some of the drawbacks of manual derivation and significantly improve computational efficiency and accuracy. In the past, the relationship among three anomalies was generally represented in the form of a trigonometric series with the first eccentricity e as the parameter. In this paper, the trigonometric series with the parameter m = 1 − 1 − e 2 e is used, as determined by the Lagrange conjugate series. We can use the formula of the Lagrange conjugate series to derive the relationship between the true anomaly and elliptic anomaly. And the relationship between the elliptic anomaly and the mean anomaly is derived by using the symbolic iteration method. In this research paper, we calculated the accuracy of the trigonometric series expansion among three types of anomalies at the first eccentricity e equal to values of 0.01, 0.1, and 0.2. The calculation results indicate that the accuracy of the trigonometric series expansion with m as the parameter is better than 10−5. Moreover, in some cases, the trigonometric series expansion among the three anomalies with m as a parameter is simpler in form than the expansion expressed with parameter e. This paper also derived and calculated the symbolic expressions and extreme values of the difference among three anomalies and expressed the extreme values of the difference in the form of a power series of e. It can be seen that the extreme value increases with the increase in eccentricity e. And the absolute values of the extreme value of the difference between the elliptic anomaly and the mean anomaly, the true anomaly and the elliptic anomaly, and the true anomaly and the mean anomaly increase in this order. When the eccentricity is small, the absolute value of the extreme value of the difference between the true anomaly and the mean anomaly is about twice as large as the elliptic anomaly and the mean anomaly and the true anomaly and the mean anomaly. [ABSTRACT FROM AUTHOR]

Increasing weather variability and corresponding increased threat to the sustainability of the system and to the food security of any nation raises the importance of weather analysis in a range of studies. Meteorological data, hence, is used as a key component while developing a weather-based risk assessment and impact assessment models. However, despite of the availability of global meteorological data in real time and several state-of the art dynamic prediction system, such models demand downscaling of these datasets to the regions of interest. The present scientific fraternity has been able to provide a range of datasets at needed spatial resolution, which are generated through interpolation, weather generation methods, satellite-based remote sensing methods, and others. Each of the datasets has their own advantages and limitations. They are not universal, because of which their robustness and reproducibility varies with location. Therefore, the present study is basically evaluation of the freely available data sources (Grid IMD, NASA POWER and MarkSim) to know which one fits best to the study area. Statistical techniques such as error statistics, correlation analysis, anomaly, and percent deviation have been used for weather dataset at three timescales (daily, weekly, and monthly). Results for maximum and minimum temperature indicated that NASA POWER datasets are more reliable than IMD data for Ranichauri (at all the three timescales) and Roorkee (only at daily and weekly timescale), unlike Udham Singh Nagar for which IMD gives better results for daily data; and MarkSim at weekly and monthly scale. It was also observed that for Udham Singh Nagar and Roorkee, MarkSim results are found to be better for RCP 2.6 as well as RCP 4.5 at higher timescales. Better performance of Tmax under RCP 4.5 indicates that the emission activities have increased in the districts, which can be attributed directly to the increased industrial establishments in the region. [ABSTRACT FROM AUTHOR]

ABSTRACT: This study aimed to investigate the relationship between serum vitamin and mineral levels and congenital defects in digestive and urogenital system anomalies in calves, lambs, and kids. The study material consisted of 13 calves, 15 lambs, 10 kids clinically and radiologically diagnosed with congenital digestive and urogenital system anomalies and 10 newborn clinically healthy calves, 10 lambs, and 10 kids. Congenital defects were diagnosed by clinical and radiological examination. Blood samples were collected from all animals, and sera were extracted for biochemical analysis. Vitamins A, D, and E, calcium, phosphorus, sodium, potassium, chlorine, magnesium, copper, iron, zinc, selenium, and manganese levels were measured in serum samples. Penile urethral diverticulum in kids, atresia ani, atresia ani with vaginal fistula in lambs, and atresia ani and atresia coli defects in calves were determined. Copper levels were higher, and zinc levels were lower in kids with penile urethral diverticulum compared to the control group. Vitamin A levels were lower in lambs with digestive system anomalies compared to the control group. Meanwhile, copper levels were higher in lambs with digestive system anomalies. Vitamin A and D levels were lower in calves with digestive system anomalies compared to the control group. There was no difference in the levels of the other parameters compared to the control group. In conclusion, insufficient serum vitamin A levels may play a role in the etiopathogenesis of congenital intestinal atresia in calves and lambs. Therefore, we believe that parenteral vitamin A administration to the mother, especially in the last trimester of pregnancy in regions with continental climates and poor green vegetation, would be beneficial. Further research should be conducted to determine the role of vitamin A in the etiopathogenesis of congenital atresia ani and coli.

Introduction: We encountered a colon cancer case with a very rare anomaly of the middle colic artery (MCA) originating from the splenic artery (SA). Case Presentation: A woman was referred to our hospital for transverse colon cancer. Three-dimensional computed tomography (3D-CT) angiography showed an anomalous MCA originating from the SA rather than from the superior mesenteric artery (SMA) as is typical. Laparoscopic left hemicolectomy with D3 lymph node dissection was performed. The lymph nodes around the SMA were dissected from the caudal view, confirming the absence of a typical MCA. An anomalous SA-originating MCA was identified just below the pancreas, where it was clipped and ligated; subsequently, total mesenteric excision was achieved. Conclusion: As D3 lymph node dissection for transverse colon cancer is technically difficult, 3D-CT angiography is useful for identifying vascular anomalies preoperatively, thereby avoiding intraoperative injury. This is the first case report of laparoscopic colectomy associated with a SA-originating MCA anomaly.

Pregnancy in the uterus rudimentary horn (PURH) is a rare form of ectopic pregnancy that develops in women suffering from abnormalities in the development of growth and fusion of the Mullerian paramesonephral ducts. A literature search was conducted in the Web of Science, eLIBRARY.ru, Scopus, PubMed/MEDLINE databases using key words “ectopic pregnancy”, “anomaly”, “complication”, “rudimentary uterine horn”, “diagnosis”, “management”. We selected articles published from 2014 to 2022. A methodological filter was not used to exclude the omission of suitable articles. The study included full-text papers and literary reviews on the subject under study, articles that are not directly related to the topic of PURH were excluded. In order to avoid the inclusion of duplicate publications in the literature review, if two papers by the same authors were found, the study period of each author was studied, and if the dates coincided, the most recent publication was selected. Usually, the PURH is overlooked and can lead to such a formidable complication as hemoperitoneum. Diagnosis and treatment of PURH remains a difficult task. Many women with rudimentary uterine horn have an acute rupture of the uterus during pregnancy. Early diagnosis is the key to successful treatment. Clinicians of various specialties should maintain a high degree of vigilance to prevent morbidity associated with this life-threatening condition.

The Cuvette Centrale is a sedimentary basin stretching from the Democratic Republic of the Congo to the Republic of Congo, Gabon and Angola. The existence of hydrocarbons in this large basin is demonstrated by the presence of indications on the DR Congo side and by the discovery of hydrocarbons by the Ngoki well (Republic of Congo) diagonally across the Lokoro sub-basin (DR Congo). In view of this evidence, the four boreholes drilled in the DR Congo's Cuvette Centrale have not reported the presence of hydrocarbons. With this in mind, this study reinterprets the geophysical data (magnetism and gravimetry) to highlight geological structures of petroleum interest. After processing and interpretation, we understand that the region contains interesting structures linked to hydrocarbon trapping. These include anticlines and faults. We discovered that there is a close relationship between the geological structures of these two countries (DR Congo and R Congo). They are all made up of the same geological structures separated by two major strike-slip faults crossed by the Congo River (NE-SW direction). These are the Dextre Fault, which displaces the compartments on the DR Congo side in a SW-NE direction, and the Senestre Fault, which runs NE-SW.

Introduction: Pancreas divisum is the most common congenital anomaly of the pancreas. It occurs due to failure of fusion of the dorsal and ventral pancreatic buds in the 7th week of intrauterine life, and leads to the formation of a large dorsal pancreatic duct and a small ventral pancreatic duct. Different types of pancreas divisum had been reported in the literature due to varying patterns of fusion of the dorsal and ventral pancreatic ducts. Aim: The present study had been performed to find out the anatomical variations of pancreas divisum, pancreatic ducts and associated congenital anomalies. Materials and Methods: The present cross-sectional retrospective study was carried out in the Department of Radiology, Gauhati Medical College and Hospital, Guwahati, Assam from January 2022 to March 2023. The Magnetic Resonance Cholangiopancreatography (MRCP) plates of 159 patients were collected and studied for variations of pancreas divisum, pancreatic ducts and associated congenital anomalies. Results: In the present study, 34 (29.8%) cases had Type-1 (classical), 5 (4.4%) cases had Type-2 and 75 (65.9%) cases had Type-3 pancreas divisum. Also, 105 (66.03%) cases had Type-1 pancreatic duct and 34 (21.4%) cases had Type-4 pancreatic duct. Four (21%) cases had associated Komi Type-2A, a choledochal cyst. Conclusion: In the present study, Type-3 pancreas divisum was most common. Also, most cases had Type-1 Major Pancreatic Duct (MPD). Choledochal cysts were found to be mostly associated with pancreas divisum.

Human behavior is regarded as one of the most complex notions present nowadays, due to the large magnitude of possibilities. These behaviors and actions can be distinguished as normal and abnormal. However, abnormal behavior is a vast spectrum, so in this work, abnormal behavior is regarded as human aggression or in another context when car accidents occur on the road. As this behavior can negatively affect the surrounding traffic participants, such as vehicles and other pedestrians, it is crucial to monitor such behavior. Given the current prevalent spread of cameras everywhere with different types, they can be used to classify and monitor such behavior. Accordingly, this work proposes a new optimized model based on a novel integrated wavelet-based channel augmentation unit for classifying human behavior in various scenes, having a total number of trainable parameters of 5.3 m with an average inference time of 0.09 s. The model has been trained and evaluated on four public datasets: Real Live Violence Situations (RLVS), Highway Incident Detection (HWID), Movie Fights, and Hockey Fights. The proposed technique achieved accuracies in the range of 92% to 99.5% across the used benchmark datasets. Comprehensive analysis and comparisons between different versions of the model and the state-of-the-art have been performed to confirm the model’s performance in terms of accuracy and efficiency. The proposed model has higher accuracy with an average of 4.97%, and higher efficiency by reducing the number of parameters by around 139.1 m compared to other models trained and tested on the same benchmark datasets.

ABSTRACTEnvironmental changes are significantly modifying terrestrial vegetation dynamics, with serious consequences on Earth system functioning and provision of ecosystem services. Land conditions are an essential element underpinning global sustainability frameworks, such as the Sustainable Development Goals (SDGs), requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces. At the global scale, long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate. However, greening trends at the national scale have received less attention, although countries like Switzerland are prone to important changing climate conditions. Hereby, we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index (NDVI) to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature, precipitation, and land cover to investigate possible responses of changing climatic conditions. Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61% significant pixels across Switzerland. In particular, the seasonal mean NDVI shows an important break for winter, autumn and spring seasons starting from 2010, potentially indicating a critical point of changing land conditions. At biogeographical scale, we observed an apparent clustering (Jura-Plateau; Northern-Southern Alps; Eastern-Western Alps) related to landscape characteristics, while forested land cover classes are more responsive to NDVI changes. Finally, the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation. The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions. This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.

Human behavior is regarded as one of the most complex notions present nowadays, due to the large magnitude of possibilities. These behaviors and actions can be distinguished as normal and abnormal. However, abnormal behavior is a vast spectrum, so in this work, abnormal behavior is regarded as human aggression or in another context when car accidents occur on the road. As this behavior can negatively affect the surrounding traffic participants, such as vehicles and other pedestrians, it is crucial to monitor such behavior. Given the current prevalent spread of cameras everywhere with different types, they can be used to classify and monitor such behavior. Accordingly, this work proposes a new optimized model based on a novel integrated wavelet-based channel augmentation unit for classifying human behavior in various scenes, having a total number of trainable parameters of 5.3 m with an average inference time of 0.09 s. The model has been trained and evaluated on four public datasets: Real Live Violence Situations (RLVS), Highway Incident Detection (HWID), Movie Fights, and Hockey Fights. The proposed technique achieved accuracies in the range of 92 % to 99.5 % across the used benchmark datasets. Comprehensive analysis and comparisons between different versions of the model and the state-of-the-art have been performed to confirm the model's performance in terms of accuracy and efficiency. The proposed model has higher accuracy with an average of 4.97 % , and higher efficiency by reducing the number of parameters by around 139.1 m compared to other models trained and tested on the same benchmark datasets. [ABSTRACT FROM AUTHOR]