Manual Intervention Research Articles

AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of bioremediation by enabling real-time monitoring and optimization of environmental cleanup processes. This paper explores the integration of AI-driven monitoring systems with bioremediation techniques, focusing on how real-time data analysis and predictive modelling can enhance the effectiveness of pollutant degradation. These AI systems continuously collect data from contaminated sites, such as soil and water, and analyse variables like microbial activity, pollutant concentration, and environmental conditions. By processing this data, AI models can optimize the bioremediation environment, adjusting factors such as pH, temperature, and nutrient levels to maximize microbial efficiency. Predictive modelling plays a crucial role in forecasting remediation outcomes, allowing for proactive adjustments to improve the speed and success of the process. The study also highlights the potential of AI in reducing operational costs by automating data collection and analysis, minimizing the need for manual intervention. Furthermore, it discusses challenges related to data quality, system integration, and the scalability of AI applications in real-world bioremediation projects. By leveraging AI's capability to provide real-time insights and predictive analytics, this research demonstrates its potential to significantly enhance the precision and sustainability of bioremediation efforts, paving the way for smarter environmental management.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

Automatic identification of coronary stent in coronary calcium scoring CT using deep learning

Coronary artery calcium (CAC) scoring CT is a useful tool for screening coronary artery disease and for cardiovascular risk stratification. However, its efficacy in patients with coronary stents, who had pre-existing coronary artery disease, remains uncertain. Historically, CAC CT scans of these patients have been manually excluded from the CAC scoring process, even though most of the CAC scoring process is now fully automated. Therefore, we hypothesized that automating the filtering of patients with coronary stents using artificial intelligence could streamline the entire CAC workflow, eliminating the need for manual intervention. Consequently, we aimed to develop and evaluate a deep learning-based coronary stent filtering algorithm (StentFilter) in CAC scoring CT scans using a multicenter CAC dataset. We developed StentFilter comprising two main processes: stent identification and false-positive reduction. Development utilized 108 non-enhanced echocardiography-gated CAC scans (including 74 with manually labeled stents), and for false positive reduction, 2063 CAC scans with significant coronary calcium (average Agatston score: 523.8) but no stents were utilized. StentFilter’s performance was evaluated on two independent internal test sets (Asan cohort- and 2; n = 355 and 396; one without coronary stents) and two external test sets from different institutions (n = 105 and 62), each with manually labeled stents. We calculated the per-patient sensitivity, specificity, and false-positive rate of StentFilter. StentFilter demonstrated a high overall per-patient sensitivity of 98.8% (511/517 cases with stents) and a false-positive rate of 0.022 (20/918). Notably, the false-positive ratio was significantly lower in the dataset containing stents (Asan cohort-1; 0.008 [3/355]) compared with the dataset without stents (Asan cohort-2; 0.043 [17/396], p = 0.008). All false-positive identifications were attributed to dense coronary calcifications, with no false positives identified in extracoronary locations. The automated StentFilter accurately distinguished coronary stents from pre-existing coronary calcifications. This approach holds potential as a filter within a fully automated CAC scoring workflow, streamlining the process efficiently.

Implementing and Evaluating Manualized Interventions in U.S. Organizations Serving Youth with Foster Care Experience: Lessons Learned from the Field

Implementing and Evaluating Manualized Interventions in U.S. Organizations Serving Youth with Foster Care Experience: Lessons Learned from the Field

Research on Artificial Self-Recovery Technology in Engineering Practice

With the development of mechanical systems in the industrial era, there is an increasing emphasis on the safety and reliability of mechanical equipment. The theory of artificial self-recovery technology has emerged, aiming to enable mechanical equipment to autonomously prevent and repair faults by simulating biological self-recovery mechanisms, thereby enhancing the safety and reliability of industrial production, reducing manual intervention, and promoting the intelligent development of manufacturing. The article mainly explores the application of artificial self-recovery theory in mechanical equipment, elaborating on the achievements in self-recovery technologies such as automatic balancing technology, compensation and self-protection technology for large systems, equipment health assistive technology, and active control technology for sealing devices and hydrostatic bearings, as well as self-repair and self-cleaning technologies. Finally, the paper looks forward to the future development of artificial self-recovery technology, believing that with technological advancements it will play an increasingly important role in the industrial field and promote the development of manufacturing towards self-recovery.

Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN)

With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises if the fluorophores employed exhibit low brightness and a low signal-to-noise ratio (SNR). Consequently, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis, which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using deep learning. We demonstrate the program’s ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na+ and Ca2+ imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types.

Comparison of active release technique and post-isometric relaxation in treatment of plantar fasciitis among traffic police wardens: a quasi-experimental study

BackgroundPlantar fasciitis (PF) is a common cause of chronic foot pain that leads to functional limitations in individuals. There has been recent research on plantar fasciitis, but no studies have compared the effects of the active release technique and post-isometric relaxation in patients to determine which manual therapy technique improves foot pain and function.ObjectiveThis study aimed to compare the effectiveness of the active release technique and post-isometric relaxation on proximal foot pain and functional status and to assess muscular tightness among patients with traffic police wards and plantar fasciitis.MethodsA quasi-experimental study (pre-test, post-test) was conducted in the Physiotherapy Department of Police Hospital, Gujranwala, Pakistan, between October 2022 and April 2023. We included 30 consecutively aged 25- to 60-year-old symptomatic police wardens diagnosed with planter fasciitis, who complained of pain on walking and standing for more than 3 months and met the inclusion criteria. They were equally assorted into two groups (n = 15 in each group) using convenient sampling. Group A received an active release exercise technique, group B received a post-isometric relaxation technique, and the participants in both groups received a similar baseline treatment protocol that consisted of calf stretching and stretching of the plantar fascia (5 repetitions and holding for 15 s). In the outcome measures, we included the Foot Function Index to assess the function of the foot and the Visual Analog Scale to examine the pain intensity measured during rest. The participants in both groups performed the selective protocol 3 days per week for 3 weeks, and we calculated the results at baseline and on the third day of the third week. We analyzed the data using SPSS software version 24 (IBM Corp., Armonk, NY). We compared within-group differences in the VAS score and FFI using paired t tests and analyzed between-group differences in the VAS score and FFI before and after performing the ART and PIR using independent t tests.ResultsThe 8-week intervention therapy revealed a demographic age mean and standard deviation of 36.60 ± 6.11 for group A and 35.60 ± 4.85 for group B. Within-group analysis demonstrated a significant decrease in VAS (pain) score following ART intervention (1.80 ± 0.862, p < 0.01) compared to PIR (6.07 ± 0.884). Between-group analysis indicated a significant reduction in FFI (disability) scores for group A (30.33 ± 3.200, p < 0.03) compared to group B (56.93 ± 4.773). Moreover, ART intervention exhibited significant improvement in VAS scores (p < 0.05), with 95% CI [1.49, 2.11]. The mean difference in FFI scores was substantially greater in the ART group (86.00 to 30.33) compared to the PIR group (p > 0.05), suggesting non-significant differences.ConclusionThe active release exercise intervention is more effective than post-isometric relaxation in improving pain and foot function disability in patients with plantar fasciitis. The present study aimed to help physical therapists choose the most efficient manual therapy exercise intervention to improve functional limitations.

Internet of Things and Distributed Computing Systems in Business Models

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

Spatial evolution characteristics of the mechanical properties of gangue-cemented paste backfill with high-water-content materials: an experimental investigation

ABSTRACT The mechanical properties of cemented paste backfill (CPB) directly determine the effect of backfilling in coal mining. However, the current studies on the mechanical properties of CPB are all based on standard cylinder or cube specimens, which cannot accurately characterise the distribution of mechanical properties in different spatial positions of CPB in situ. In addition, high-water-content material (HWM) has developed rapidly as a new binder in recent years. Therefore, in this paper, the uniaxial compressive strength (UCS) and elastic modulus of CPB with gangue as the aggregate and HWM as the binder (GHW-CPB) were investigated under different gangue particle sizes. The following conclusions were drawn: (1) The spatial evolution and distribution characteristics of mechanical properties of GHW-CPB are analysed. (2) The spatial evolution regression models of mechanical properties of GHW-CPB are established. (3) We improved the GHW-CPB spatial evolution regression models for mechanical properties due to their limitations and confirmed its validity and rationality. Through the research results of this paper, the failure position of the CPB in the goaf can be accurately predicted, so that manual interventions can be carried out in the vulnerable areas in advance to reduce the risk of coal mining production.

Application of Ethephon Manually or via Drone Enforces Bud Dormancy and Enhances Flowering Response to Chilling in Litchi (Litchi chinensis Sonn.)

Ethephon (2-chloroethylphosphonic acid) is frequently used for flush management in order to maximize flowering in litchi. However, the optimal dosage of ethephon, which balances between flush control effect and the detrimental effect on leaves, is unknown. This study aimed to identify the optimal ethephon dosage and test more efficient ethephon application methods, using a drone for flush control and flowering promotion in litchi. The effects of a single manual full-tree spray of 250, 500 or 1000 mg/L of ethephon in early November on the bud break rate, leaf drop rate, net photosynthetic rate, LcFT1 expression and floral induction (panicle emergence rate and panicle number) in ‘Jingganghongnuo’ litchi were examined in the season of 2021–2022. In the season of 2022–2023, the effects of drone application of 1000 mg/L of ethephon in early November on bud growth and floral induction were observed. The results showed that the manual ethephon treatments were effective at enforcing bud dormancy and elongating the dormancy period and that the effects were positively dependent on dosage. One manual spray of 1000 mg/L of ethephon in late autumn enabled a dormancy period of 6 weeks. The treatments advanced seasonal abscission of old leaves in winter and caused short-term suppression on photosynthesis within 2 weeks after treatment. Ethephon treatments, especially at 1000 mg/L, enhanced the expression of LcFT1 in the mature leaves and promoted floral induction reflected by earlier panicle emergence and increased panicle emergence rate and number in the terminal shoot. The floral promotion effect was also positively dosage dependent. The cumulative chilling hours below 15 °C from the date of treatment to the occurrence of a 20% panicle emergence rate were lowered in ethephon treatments. A drone spray of 1000 mg/L of ethephon solution consumed a sixth of the manual spray solution volume and was two thirds less effective in suppressing bud break compared with manual spraying. However, it achieved a significant flowering promotion effect comparable to traditional manual spraying. The results suggest that ethephon application enhanced flowering responsiveness to chilling as well as enforced bud dormancy. The application of ethephon with a drone proved to be an efficient method for flush control and flower promotion.

Automated Pipeline for Multi-LiDAR Extrinsic Calibration: Experimental Evaluation and Performance Analysis

Abstract. In recent years, 3D LiDAR (Light Detection and Ranging) has become a crucial sensor in various applications such as autonomous vehicles, robotics, object detection, precision forestry, and agriculture. However, specific LiDAR sensors, such as Velodyne VLP16, exhibit some drawbacks, such as a limited field of view and sparse data density, making them inadequate for certain specific applications. Hence, this research proposes a method for calibrating two VLP16 LiDAR sensors to improve coverage and reduce blind spots. The pipeline for performing the calibration begins with mounting LiDARs correctly in a rod at a specific orientation and distance, followed by the selection of multiple sites for data collection, then performing a registration algorithm for estimating calibration parameters, and then an accuracy assessment of the calibrated point clouds. The registration algorithm used here is a modified version of ICP (Iterative Closest Point), which overcomes the need for initialization and eliminates manual intervention in installing targets or retro-reflectors. Finally, we evaluated the accuracy of the fused point cloud collected in an open environment using two calibrated Velodyne VLP16 sensors. For accuracy assessment, we used the PCA eigenvalue and RMSE value to observe how tightly point clouds are fused. As a calibration result, we got the orientation and translation parameters, which are used to achieve the common coordinate system, and accomplished calibration accuracy up to single-digit precision from all the experimental sites. Now the system of two calibrated VLP16 sensors will provide higher coverage and increased data density and might be useful for forest applications.

Classification and Comparison of Data Augmentation Techniques

Data augmentation is a technical method that generates additional data samples by applying different transformations and processes to the original training data. It aims to increase the size and diversity of the dataset, thus improving the models' generalization ability. This paper provides a detailed introduction to geometric transformations, data mixing, automated data augmentation methods, and generative adversarial methods. Geometric transformations manipulate the spatial configuration of data samples to create new variants that retain the original content. Data mixing techniques blend multiple data samples to produce novel training examples, promoting robustness and reducing overfitting. Automated data augmentation methods leverage search algorithms to discover optimal augmentation policies that improve model performance without manual intervention. Generative adversarial methods employ generative adversarial networks to synthesize realistic data samples that enrich the training dataset. These methods find broad application in domains including object detection, image classification, natural language processing, and speech recognition, significantly improving model performance. The paper explores the advantages and limitations of data augmentation techniques in practical applications and identifies future research directions and challenges. By systematically classifying and selecting appropriate data augmentation methods for specific application scenarios, this review provides a theoretical foundation and practical guidance for real-world applications.

Role of Artificial Intelligence in Forensics

Artificial Intelligence (AI) is a critical domain within software engineering, focusing on computer processes capable of emulating human behaviors and cognitive processes such as learning, reasoning, adaptation and self –correction. Concurrently forensic science applies scientific methodologies to criminal investigations, specifically concentrating on analyzing evidence within criminal cases. The conventional methods of conducting autopsies and forming opinions exhibit numerous limitations, many of which can be effectively addressed through AI integration. Digital forensics stands out as one of the most rapidly advancing technologies, profoundly influencing the techniques and tools employed for analyzing, monitoring and visualizing crime scenes to mitigate emerging threats and cyber-attacks. Moreover, the realm encompasses forensic medicine, which applies medical expertise to administer justice in Legal proceedings, thereby resolving associate legal complexities. Leveraging the capabilities of AI enables the swift and through sorting of files without manual intervention, significantly expediting investigative processes. Furthermore,AI plays a pivotal role in the identification of mutilated bodies, facilitating the development of tools to reconstruct full facial features. AI generated images can provide remarkable resemblances, aiding in accurately depicting facial features to aid identification efforts. Integrating AI into forensic practices enhances efficiency, accuracy and overall effectiveness in resolving criminal cases

Advancements in smart agriculture through innovative weed management using wavelet-based convolution neural network

Smart agriculture has shifted the paradigm by integrating advanced technologies, particularly weed management. This paper introduces an innovative approach to weed control by applying a Wavelet-based Convolution Neural Network (WCNN). In the era of precision agriculture, our study explores the integration of WCNN into real-world scenarios, emphasizing its adaptability to diverse environmental conditions. Utilizing the spatial-frequency analysis features of wavelets and convolutional neural networks, the WCNN model is the most effective at finding weeds, classifying them, and managing them specifically in agricultural fields in real-time. This research contributes to the scientific discourse on smart agriculture and addresses the challenges of invasive weeds, presenting a sustainable solution for optimizing resource utilization. Our investigation includes a detailed exploration of WCNN’s adaptive learning mechanisms and dynamic adjustment to changing agricultural landscapes. The model seamlessly integrates with existing smart farming infrastructure, showcasing a substantial reduction in manual intervention and a simultaneous increase in agricultural productivity. We incorporate fog computing and resource optimization into our framework, enhancing the efficiency of onboard data processing. To evaluate the real-world efficacy of WCNN, we conducted comprehensive experiments in texture classification and image labelling using two distinct datasets: the plant seedling and soybean weed datasets. Results demonstrate the superior performance of WCNN, achieving higher accuracy in training and test scenarios with significantly fewer parameters than traditional CNNs. For the soybean weed dataset, WCNN achieved remarkable accuracy in the training (0.9970) and testing (0.9987) phases, with correspondingly low losses of 0.0109 and 0.0048. The WCNN model demonstrated high accuracy during training (0.9739) and testing (0.9902), with minimal losses of 0.0898 and 0.0239 in the plant seedling dataset.

Measuring fidelity to manualised peer support for people with severe mental health conditions: development and psychometric evaluation of the UPSIDES fidelity scale

BackgroundPeer support workers provide support for people experiencing mental health conditions based on their own lived experience of mental health problems. Assessing fidelity to core ingredients of peer support is vital for successful implementation and intervention delivery. Modifications to its implementation are needed when scaling up to different socio-economic settings, raising further uncertainty about fidelity. As part of a large multi-centre study on peer support called Using Peer Support In Developing Empowering Mental Health Services (UPSIDES), we developed and evaluated the psychometric properties of the UPSIDES Fidelity Scale.MethodsWe constructed the fidelity scale based on an initial item pool developed through international expert consultation and iterative feedback. Scale refinement involved site-level expert consultation and translation, resulting in a service user-rated 28-item version and a peer support worker-rated 21-item version assessing receipt, engagement, enactment, competence, communication and peer support-specific components. Both versions are available in six languages: English, German, Luganda, Kiswahili, Hebrew and Gujarati. The scale was then evaluated at six study sites across five countries, with peer support workers and their clients completing their respective ratings four and eight months after initial peer support worker contact. Psychometric evaluation included analysis of internal consistency, construct validity and criterion validity.ResultsFor the 315 participants, item statistics showed a skewed distribution of fidelity values but no restriction of range. Internal consistency was adequate (range α = 0.675 to 0.969) for total scores and all subscales in both versions. Confirmatory factor analysis indicated acceptable fit of the proposed factor structure for the service user version (χ2/df = 2.746; RMSEA = 0.084) and moderate fit for the peer support worker version (χ2/df = 3.087; RMSEA = 0.093). Both versions showed significant correlations with external criteria: number of peer support sessions; perceived recovery orientation of the intervention; and severity of illness.ConclusionsThe scale demonstrates good reliability, construct and criterion validity, making it a pragmatic and psychometrically acceptable measure for assessing fidelity to a manualised peer support worker intervention. Recommendations for use, along with research and practical implications, are addressed. As validated, multi-lingual tool that adapts to diverse settings this scale is uniquely positioned for global application.Trial registrationISRCTN, ISRCTN26008944. Registered on 30 October 2019.

ANÁLISE DE UM PROTÓTIPO DE UMA ESTEIRA TRANSPORTADORA COM AUTOMAÇÃO INDUSTRIAL PARA SEPARAÇÃO DE PEÇAS METÁLICAS E NÃO METÁLICAS UTILIZANDO ARDUINO UNO

Introduction: The use of Arduino Uno and the LS PLC allows for an innovative approach to process automation, offering a practical and effective solution for industrial applications. This automated system is designed to increase productivity and accuracy in material separation, minimizing manual intervention and associated errors. Objective: to analyze a prototype of a conveyor belt with industrial automation for separating metal and non-metal parts using Arduino. Methodology: This study is a qualitative case study of the development of a prototype conveyor belt with industrial automation using the arduino system. Results: The data showed that the NPN inductive and ultrasonic sensors had high detection accuracy rates, with 95% and 97% success, respectively. Communication between the Arduino and the PLC was efficient, allowing the system to respond quickly to sensor readings and trigger the actuators synchronously. The implementation of safety devices, such as pushbuttons and circuit breakers, ensured system protection, while the visual monitoring provided by the 16×2 LCD display facilitated real-time monitoring of operations. Conclusion: The automation of the conveyor belt proved to be a viable and effective solution for sorting materials in an industrial context. The integration of the components and the efficient communication between them were fundamental to the success of the project. This project serves as a solid foundation for future innovations in industrial automation, contributing to the modernization and efficiency of production processes.

ANÁLISE DE UM PROTÓTIPO DE UMA ESTEIRA TRANSPORTADORA COM AUTOMAÇÃO INDUSTRIAL PARA SEPARAÇÃO DE PEÇAS METÁLICAS E NÃO METÁLICAS UTILIZANDO ARDUINO UNO

Introduction: The use of Arduino Uno and the LS PLC allows for an innovative approach to process automation, offering a practical and effective solution for industrial applications. This automated system is designed to increase productivity and accuracy in material separation, minimizing manual intervention and associated errors. Objective: to analyze a prototype of a conveyor belt with industrial automation for separating metal and non-metal parts using Arduino. Methodology: This study is a qualitative case study of the development of a prototype conveyor belt with industrial automation using the arduino system. Results: The data showed that the NPN inductive and ultrasonic sensors had high detection accuracy rates, with 95% and 97% success, respectively. Communication between the Arduino and the PLC was efficient, allowing the system to respond quickly to sensor readings and trigger the actuators synchronously. The implementation of safety devices, such as pushbuttons and circuit breakers, ensured system protection, while the visual monitoring provided by the 16×2 LCD display facilitated real-time monitoring of operations. Conclusion: The automation of the conveyor belt proved to be a viable and effective solution for sorting materials in an industrial context. The integration of the components and the efficient communication between them were fundamental to the success of the project. This project serves as a solid foundation for future innovations in industrial automation, contributing to the modernization and efficiency of production processes.

Effectiveness Of Positional Release Therapy And Muscle Energy Technique On Glenohumeral Impingement Among The Elite Swimmers

Background: Glenohumeral impingement is one of the common clinical conditions among the overhead athletes. Elite swimmers develop impingement at the glenohumeral joint due to overuse injuries. This type of impingement is due to lack of muscle strength, demanding training session and altered flexibility. Muscle energy technique and positional release therapy are two different manual therapy interventions that may improve joint range and glenohumeral function. Aim: The aim of the study is to compare the effectiveness of positional release therapy and muscle energy technique on glenohumeral impingement among the elite swimmers. Methods: The study included about 40 elite swimmers in which group A (n=20) received positional release therapy with strengthening exercise and group B (n=20) received muscle energy technique with strengthening exercise. The intervention was given for the duration of 4 weeks and the outcome measures used were numerical pain rating scale, range of motion of the glenohumeral joint and the shoulder pain and disability index. Results: The results of the present study were analyzed using Mann Whitney U test and Wilcoxon signed rank test. The pre and post-test values were taken. The results from analysis shows both groups shows statistically significant improvement but group A show highly significant improvement in reducing pain, improving range of motion and shoulder functional ability comparing to group B,‘p’ value &lt; 0.05 Conclusion: The study concluded that positional release therapy was effective clinically and statistically shows significant improvement in reducing pain, improving Glenohumeral range of motion and shoulder function.

Dystocia due to Twin Pregnancy in HF Crossbred Cattle- A Case Report

Background: Freemartinism is one of the most commonly found intersex conditions in cattle, although it may also occur in small ruminants. The freemartin phenotype appears in a dizygotic twin pregnancy where one twin is a male and the other is a female. Due to precocious anastomoses between the placental vascular systems of the two fetuses, masculinising molecules reach the female twin and disrupt the normal sexual differentiation. In cattle, this condition is observed in 90 to 97% of twin pregnancies. A freemartin is, by definition, a genetically female fetus masculinised in the presence of a male co-twin, giving rise to a sterile heifer. Methods: A HF crossbred cow of 2nd parity was presented at Veterinary Clinical Complex, College of Veterinary Science and Animal Husbandry, Mhow (M.P) with the history of difficulty in calving, uterine contraction since last night, restlessness, uterine fluid discharge from vagina and swollen vulva. Per vaginal examination revealed presence of two fetuses in the uterus one being in anterior while other being in posterior presentation. Result: Dystocia was resolved by manual intervention per vaginum resulting in birth of one male and one female calf. The male calf was strong and active, while the female calf was weak as compare to male.

Glassy Carbon Fiber‐Like Multielectrode Arrays for Neurochemical Sensing and Electrophysiology Recording

AbstractReal‐time measurement of neurochemical and electrophysiological activities in the living brain is vital for advancing neuroscience and understanding brain disorders. Carbon fiber microelectrodes (CFEs) have been widely utilized for in vivo electrochemical detection due to their subcellular size, biocompatibility, and desirable electrochemical properties, and CFE arrays have demonstrated excellent multi‐site chronic sensing of dopamine (DA) and neural activity recording capabilities. However, manual fabrication of CFE arrays lacks reproducibility and batch production capacity. Alternatively, photolithography enables batch fabrication of glassy carbon (GC) multielectrode arrays (GC‐MEAs) with high resolution and reproducibility, but the insertion of planar flexible MEAs poses challenges. In this study, GC fiber‐like (GCF) MEAs are presented and fabricated using photolithography. The GCF MEAs feature fiber‐like GC electrodes with small cross‐sections, facilitating self‐insertion into brain tissue without additional aids. Batch fabrication of GCF MEAs allows for customizable designs with minimal manual intervention. Comparative analysis with CFEs demonstrates improved electrochemical properties of GCF. In vivo experiments in mouse brains confirm the GCF MEAs' ability to measure neurotransmitter concentrations and record neural activities. This novel fabrication approach is promising for multimodal electrical and chemical measurements with minimal footprint, offering significant advancements in neural interface technology for neuroscience research and clinical applications.