bluuebunny/crossref_metadata_2025 huggingface dataset:
Can Speaking More Than One Language Help Prevent Alzheimer’s Disease?
Authors: Mendez Mario F.
Published: August 2023

Alzheimer’s disease (AD) is an age-related neurocognitive disorder that is epidemic in the elderly population. Currently, there are limited pharmacological interventions, and this has heightened the urgency to identify potential preventable or modifiable risk factors that promote resilience to the neuropathological effects of AD. The regular use of two or more languages is one such factor that may increases cognitive reserve through the long-standing executive control involved in managing multiple languages in the brain. There is also evidence that bilingualism is associated with increased brain reserve or maintenance, particularly in frontal-executive structures and networks. This review examines the current, sometimes conflicting literature on bi/multilingualism and AD. These studies have confounding variations in the assessment of age of second language onset, language proficiency, language usage, and whether determining incidence of AD or age of symptom onset. Despite these limitations, most publications support the presence of increased frontal-executive reserve that compensates for the development of AD neuropathology and, thereby, delays the emergence of clinical symptoms of dementia by about 4-5 years. Although regularly speaking more than one language does not protect against AD neuropathology, the delay in its clinical expression has a potentially significant impact on the lifelong morbidity from this age-related disease. Learning other languages may be an important modifiable factor for delaying the clinical expression of AD in later life.
Minimizing non-processing energy consumption/total weighted tardiness & earliness, and makespan into typical production scheduling model-the job shop scheduling problem
Authors: Jyothi Kilari , Dubey R.B.
Published: August 2023

This manuscript proposes a hybrid method to solve the job shop scheduling problem (JSP). Here, the machine consumes different amounts of energy for processing the tasks. The proposed method is the joint execution of Feedback Artificial Tree (FAT) and Atomic Orbital Search (AOS), hence it is called the FAT-AOS method. The aim of the proposed multi-objective method is to lessen the non-processing energy consumption (NEC), total weighted tardiness and earliness (TWET), and makespan (Cmax). Depending on the machine’s operating status, such as working, standby, off, or idle, the energy-consumption model of the machine is constructed. The NEC is the essential metric and the Cmax and TWET are the classical performance metrics used to predict the effects of energy effectiveness in JSP. The proposed AOS technique optimizes the objective of the system and FAT is used to predict the optimal outcome. The proposed method’s performance is implemented in MATLAB and is compared with various existing methods. From this simulation, under the 15x15_1 instance, the proposed method makes the span the best value of 1370, the median is 1720, and the worst value become 2268 is obtained.
Hybrid fuzzy and gated recurrent network based artificial intelligence approach for fault diagnosis and prognosis of transformers using dissolved gas analysis
Authors: Krishna Veni K.S. , Senthil Kumar N. , Srinivas R.
Published: August 2023

In the electrical energy transmission and distribution sector, power transformers play an important role. Early fault diagnosis and prognosis are essential to ensure continuous operation and also to prepare a proper maintenance schedule based on the requirements. The occurrence of a fault in the transformer will lead to the formation of various gases inside the transformer tank. For fault diagnosis in the transformer, Dissolved Gas Analysis (DGA) is an excellent method. An Artificial Intelligence (AI) based fault diagnosis and prognosis system using dissolved gases in transformer oil is helpful to predict the health state of the transformer well in advance. Hence, based on the fault severity level, the remaining useful life of the transformer, fault type and current state of the transformer can be estimated effectively by imparting AI to the existing system. A Two-Tier Fuzzy Logic Controller (TTFLC) is proposed in this article to find the type of fault and health index (HI) of the transformer. For further fault prognosis, an effective Gated Recurrent Network (GRN) based deep learning enabled future learning estimator is used for predicting the Criticality Index (CI) of the Transformer. The performance of the proposed method is evaluated for both data from the IEEE data set and expert data collected from the southern Tamil Nadu region. The proposed system shows better results even in multivariate, complex process systems. The diagnosis accuracy of the proposed system is obtained as 95.28% and it compared with conventional methods such as Rogers Ratio Method (RRM), Duval Triangle Method (DTM) and Duval Pentagon Method (DPM) and other AI based methods such as Radial Basis Neural Network (RBNN), k-nearest neighbors (KNN). The diagnosis accuracy of other conventional and AI based methods are less than 90% for the collected dataset.
A new mobile data collection and mobile charging (MDCMC) algorithm based on reinforcement learning in rechargeable wireless sensor network
Authors: Soni Santosh , Chandra Pankaj , Singh Devendra Kumar , Sharma Prakash Chandra , Saini Dinesh
Published: August 2023

Recent research emphasized the utilization of rechargeable wireless sensor networks (RWSNs) in a variety of cutting-edge fields like drones, unmanned aerial vehicle (UAV), healthcare, and defense. Previous studies have shown mobile data collection and mobile charging should be separately. In our paper, we created an novel algorithm for mobile data collection and mobile charging (MDCMC) that can collect data as well as achieves higher charging efficiency rate based upon reinforcement learning in RWSN. In first phase of algorithm, reinforcement learning technique used to create clusters among sensor nodes, whereas, in second phase of algorithm, mobile van is used to visit cluster heads to collect data along with mobile charging. The path of mobile van is based upon the request received from cluster heads. Lastly, we made the comparison of our proposed new MDCMC algorithm with the well-known existing algorithms RLLO [32] & RL-CRC [33]. Finally, we found that, the proposed algorithm (MDCMC) is effectively better collecting data as well as charging cluster heads.
Blizzard prediction in antarctic meteorological data using deep learning techniques
Authors: Samy V.S. , Thenkanidiyoor Veena
Published: August 2023

Due to the unpredictable nature of the weather and the complexity of atmospheric movement, extreme weather has always been a significant and challenging meteorological concern. Meteorological problems and the complexity of how the atmosphere moves have made it necessary to find a technological solution. Deep learning techniques can automatically learn and train from vast quantities of data to provide enhanced feature expression. This is frequently used in computer vision, natural language processing, and other domains to enhance the performance of numerous real-time problems. The purpose of this research is to propose a deep learning-based approach for effectively predicting extreme weather events such as blizzards. To recognize weather patterns and forecast blizzards, the proposed deep learning-based method primarily employs RNN with LSTM. Real-time datasets from the Polar Regions were used to test the proposed approach’s accuracy, and tests were conducted to compare it to existing weather forecasting models. The accuracy of the model is 49.60% (univariate) and 55.19% (bivariate) using bivariate attributes of wind speed and air pressure based on the calculated RMSE values such as 0.0023 and 0.0021.
Progressive Dunhuang murals inpainting based on recurrent feature reasoning network
Authors: Li Jiacheng , Wang Jianhua , Liu Wenjie , Gao Shengxia , Du Shiqiang
Published: August 2023

The Dunhuang murals, notably the paintings on the interior walls of China’s Dunhuang Grottoes, are considered international cultural treasure. The Dunhuang murals were ruined to varied degrees after a lengthy period of erosion. Deep learning networks were utilized to reconstruct broken parts of murals in order to better preserve their important historical and cultural values. Due to the presence of various damages, such as large peeling, mold and scratches, and multi-scale objects in the mural, a simple porting of existing working methods is suboptimal. In this paper, we propose a progressive Dunhuang murals inpainting (PDMI) based on recurrent feature reasoning network to progressively infer the pixel values of hole centers by a progressive approach, aiming to obtain visually reasonable and semantically consistent inpainted results. PDMI consists mainly of the FFC-based recurrent feature reasoning (RFR) module and Multi-scale Knowledge Consistent Attention (MKCA) module. The RFR module first fills in the feature value at the feature map’s hole border, then utilizes the obtained feature value as a clue for further inference. The module steadily improved the limitation of hole centers, making the inpainted results more explicit; MKCA enables feature maps in RFR to handle richer background information from distant location information in a flexible manner while preventing misuse. After several round-robin inferences provide multiple feature maps, these feature maps are fused using an adaptive feature weighted fusion mechanism, then the fused feature maps decode back to RGB image. Experiments on a publicly available dataset and a self-made Dunhuang mural dataset reveal that the proposed method outperforms the comparison algorithm in both qualitative and quantitative aspects.
Comprehensive evaluation of high-quality sports industry development in the new era using fuzzy numbers intuitionistic fuzzy sets
Authors: Ge Xiaoxiang , Choi Deokhwan , Yuan Mengxian , Yang Zeyun
Published: August 2023

The swift ascension of the sports industry can be attributed to the advancements of the contemporary era, and serves as a significant indication of the industry’s progression towards a novel phase and pinnacle. Despite a delayed inception, China’s sports industry has experienced a swift evolution and has established a unique developmental framework, thereby establishing a firm groundwork for the prospective growth of China’s sports industry. The sports industry of China is currently encountering fresh prospects for growth in the contemporary era. However, it is also confronted with formidable obstacles and strains that necessitate careful handling. To attain novel breakthroughs and advancements, it remains imperative to explore innovative perspectives and avenues. The assessment of the sports industry’s high-quality development in the new era is a classic Multiple Attribute Group Decision Making (MAGDM) problem. The TODIM and VIKOR methodologies have been employed in addressing Multiple Attribute Group Decision Making (MAGDM) challenges in current times. Fuzzy number intuitionistic fuzzy sets (FNIFS) serve as a valuable instrument in the characterization of uncertain information for the comprehensive assessment of the high-quality development of the sports industry in the contemporary era. The present manuscript introduces the construction of the fuzzy number intuitionistic fuzzy TODIM-VIKOR (FNIF-TODIM-VIKOR) method, which is designed to address multiple attribute group decision making (MAGDM) problems in the context of fuzzy number intuitionistic fuzzy sets (FNIFSs). Ultimately, a numerical case study is presented to comprehensively evaluate the high-quality development of the sports industry in the new era, in order to validate the proposed methodology.
Research on flame detection method based on improved SSD algorithm
Authors: Zhan Huawei , Pei Xinyu , Zhang Tianhao , Zhang Linqing
Published: August 2023

A flame detection algorithm based on the improved SSD (Single Shot Multibox Detector) is proposed in response to the issues with the limited detection distance, delayed reaction, and high false alarm rate of previous flame detection systems. First, the ResNet-50-SPD model was added to the original backbone network to improve the detection of low resolution and tiny objects. After that, incorporate feature fusion between layers to improve the bond between contexts. Before the feature entered the prediction, the impact of channel number reduction was eliminated using the adaptive module AAM. According to experimental findings, the modified SSD algorithm’s mAP value on on the random division dataset and K-fold verification dataset reaches 87.89% and 89.63%, respectively, which is 3.97% and 5.17% higher than the original SSD, while the FPS remains at 64.9 f/s. It is helpful to improve the time of the fire alarm, find the ignition point in time, and better meet the actual engineering needs of fire monitoring.
Computer vision based human fall detection and classification for real-time videos
Authors: Jeganathan Aruna , Chellaiah Jeyalakshmi
Published: August 2023

Most recently, Human fall detection systems using deep learning models find major applications in all fields, especially in the held of healthcare. Even without doctor analysis, most Neurological and musculoskeletal diseases such as oncoming strokes and gait problems can be identified using these models and computer vision. In this article, automatic human fall detection is proposed using a convolutional neural network by applying real-time videos. In general, most of the research has been carried out using standard videos which will not apply to real-time applications. Hence this work concentrates about using convolutional neural networks as a system has real-time videos for the Human Fall Detection and monitoring system using three pre-trained models: (i) TinyYOLOv3-ones, (ii) AlphaPose and (iii) ST-GCN. The proposed Spatial temporal graph convolutional networks produce better accuracy with captured real-time video for human fall detection. The same method was also utilized for classification with different epochs. The results were compared and maximum accuracy of 100% is obtained for 500 epochs. Hence it is proved that the existing method can be utilized for human fall detection with greater accuracy.
A probabilistic neural network with adaptive capability to optimize fault trees for the intelligent fault diagnosis of rapier loom throughout its operating cycle1
Authors: Xiao Yanjun , Zhao Yue , Han Furong , Peng Kai , Wan Feng
Published: August 2023

The mechanical structures of the rapier loom are strongly coupled, resulting in faults that are characterized by strong coupling, hierarchy, phase dynamics, and a transient nature. However, current fault diagnosis methods using a single approach are not satisfactory. Additionally, fault diagnosis of the entire operation cycle of the rapier loom equipment is lacking. This paper proposes a fault tree diagnosis method with probabilistic neural network optimization to build a complete fault diagnosis system for rapier looms and improve their intelligent diagnosis capability. The method has strong fault tolerance and self-adaptive capability, allowing for accurate location of the root cause of the fault from multiple fault sources. By accumulating fault samples and continuously improving the diagnosis network, the accuracy of diagnosis can be further enhanced. Initially, the failure mechanism of key subsystems of rapier loom is analyzed. A fault tree model is established for each subsystem based on expert experience and historical data. The model identifies the characteristic sign quantities of typical fault types and serves as the basic input for fault identification. A probabilistic neural network is used to train the fault sample set and complete the diagnosis of the cause of the fault. According to field experiments, the proposed method has demonstrated a significant improvement in the efficiency of locating and identifying fault signs in rapier looms. This improvement allows for accurate and quick identification of faults.
Research on contactless intelligent medication pickup mode selection based on a hospital in China under COVID-19
Authors: Liu Xinyi , Liu Hao , Liu Yuanji
Published: August 2023

BACKGROUND: During an outbreak such as COVID-19, hospital staff needs to be in close contact with all types of patients visiting the hospital and the risk of cross-infection is extremely high. Payment and medication pickup is a mandatory part of a patient’s hospital visit, with direct contact between healthcare workers and patients, and long waiting times in the hospital area, which can easily lead to the spread of disease infection. OBJECTIVE: This paper designed the prototype of a contactless smart medicine cabinet based on RFID technology and optimized the patient consultation and medication pickup process to address these problems. METHODS: We conducted a 50-day field observation of patients for consultation and medication pickup at the First Hospital in H city, Jiangsu Province, China, and randomly timed 1600 sets of data from Surgery (ophthalmology) and Internal patients, then we designed the prototype of a contactless smart medicine cabinet based on RFID technology, optimized the patient consultation and medication pickup process, comparing the traditional and intelligent models using AnyLogic. RESULTS: The results show that this contactless medicine cabinet was able to reduce the time taken by patients in consultation and medicine pickup by 18.74 minutes, increasing the overall efficiency of the consultation by 32.20%. The simulation revealed that this contactless intelligent medication pickup model was able to reduce the time taken by patients in consultation and medicine pickup, increasing the overall efficiency of the consultation, effectively reducing the frequency of contact between healthcare workers and patients, and reducing the risk of disease infection. CONCLUSION: The proposed technical model provides a new idea to solve the problems of long queues, low efficiency and high risk of infection for patients to consult and get medicine during epidemics. Especially within hospitals it has important theoretical and practical implications for epidemic prevention and control as well as future hospital management.
Out-of-set association analysis of lung cancer drugs and symptoms based on clinical case data mining
Authors: Hong Mei , Zhao Yi-Dong , Zhong Tao-Li , Lu Ming , Sun Wen-Hao , Chen Tian-Yuan , Hong Nan , Zhu Yao , Yu Da-Hai
Published: August 2023

BACKGROUND: There are 1.8 million lung cancer deaths worldwide, accounting for 18% of global cancer deaths, including 710,000 in China, accounting for 23.8% of all cancer deaths in China. OBJECTIVE: To explore the out-of-set association rules of lung cancer symptoms and drugs through text mining of traditional Chinese medicine (TCM) treatment of lung cancer, and form medical case analysis to analyze the experience of TCM syndrome differentiation in its treatment. METHODS: The medical records of all patients diagnosed with lung cancer in Nanjing Chest Hospital from January to December 2018 were collected, and the out-of-set association analysis was performed using the MedCase v5.2 TCM clinical scientific research auxiliary platform based on the frequent pattern growth enhanced association analysis algorithm. RESULTS: In terms of TCM treatment of lung cancer, the clinical symptoms with high correlation included cough, expectoration, chest distress, and white phlegm; and the drugs with high correlation included Pinellia ternata, licorice root, white Atractylodes rhizome, and Radix Ophiopogonis; with the prescriptions based on Erchen and Maimendong decoctions. CONCLUSION: This analytical study of the medical cases of TCM treatment for lung cancer was performed using data mining techniques, and the out-of-set association rules between clinical symptoms and drugs were analyzed, including the understanding of lung cancer in TCM. Moreover, the essence of experience in drug use was gathered, providing significant scientific guidance for the clinical treatment of lung cancer.
Breast Imaging Reporting and Data System evaluation of breast lesions improved with virtual touch tissue imaging average grayscale values
Authors: Lian Weizhen , Lian Kaimei , Lin Teng
Published: August 2023

BACKGROUND: Early breast cancer diagnosis is of great clinical importance for selecting treatment options, improving prognosis, and enhancing the quality of patients’ survival. OBJECTIVE: We investigated the value of virtual touch tissue imaging average grayscale values (VAGV) helper Breast Imaging Reporting and Data System (BI-RADS) in diagnosing breast malignancy. METHODS: We retrospectively analyzed 141 breast tumors in 134 patients. All breast lesions were diagnosed pathologically by biopsy or surgical excision. All patients first underwent conventional ultrasound (US) followed by virtual touch tissue imaging (VTI). The measurement of the VAGV of the lesion was performed by Image J software. BI-RADS classification was performed for each lesion according to the US. We performed a two-by-two comparison of the diagnostic values of VAGV, BI-RADS, and BI-RADS+VAGV. RESULTS: VAGV was lower in malignant tumors than in benign ones (35.82 ± 13.39 versus 73.58 ± 42.69, P< 0.001). The area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity of VAGV was 0.834, 84.09%, and 69.07%, respectively. Among BI-RADS, VAGV, and BI-RADS+VAGV, BI-RADS+VAGV had the highest AUC (0.926 versus 0.882, P= 0.0066; 0.926 versus 0.834, P= 0.0012). There was perfect agreement between the two radiologists using VAGV (ICC= 0.9796) and substantial agreement using BI-RADS (Kappa= 0.725). CONCLUSION: Our study shows that VAGV can accurately diagnose breast cancer. VAGV effectively improves the diagnostic performance of BI-RADS.
A non-interventive monitoring system prototype for chronic patients with hypertension and asthma
Authors: Shehri Hamdan Al , Aedh Abdullah
Published: August 2023

BACKGROUND: Monitoring the vital signs of chronic patients with hypertension, asthma, and chronic obstructive pulmonary disease (COPD) aids in disease prevention. OBJECTIVE: This study enhances the patient quality of life while adding to the corpus of information about electronic medical devices. METHOD: The requirements for both the functional and non-functional system architecture were determined and designs were made. Diagrams were used to describe the system’s parts, behaviour, and connections before implementation. RESULTS: Although the project’s development produced a remote monitoring system prototype with outcomes comparable to those of patented and regarded as reliable devices, CCFHAC is not yet prepared to be considered a fully finished good that can be used to define a person’s health status with absolute certainty. CONCLUSION: This endeavour marks a step in investigating how the Internet of Things might improve the quality of life for Jordanian patients.
Pre-training and ensembling based Alzheimer’s disease detection
Authors: Xu Fan , Zheng Qihang , Shi Jia , Yan Keyu , Wang Mingwen
Published: August 2023

BACKGROUND: Alzheimer’s disease (AD) endangers the physical and mental health of the elderly, constituting one of the most crucial social challenges. Due to lack of effective AD intervention drugs, it is very important to diagnose AD in the early stage, especially in the Mild Cognitive Impairment (MCI) phase. OBJECTIVE: At present, an automatic classification technology is urgently needed to assist doctors in analyzing the status of the candidate patient. The artificial intelligence enhanced Alzheimer’s disease detection can reduce costs to detect Alzheimer’s disease. METHODS: In this paper, a novel pre-trained ensemble-based AD detection (PEADD) framework with three base learners (i.e., ResNet, VGG, and EfficientNet) for both the audio-based and PET (Positron Emission Tomography)-based AD detection is proposed under a unified image modality. Specifically, the effectiveness of context-enriched image modalities instead of the traditional speech modality (i.e., context-free audio matrix) for the audio-based AD detection, along with simple and efficient image denoising strategy has been inspected comprehensively. Meanwhile, the PET-based AD detection based on the denoised PET image has been described. Furthermore, different voting methods for applying an ensemble strategy (i.e., hard voting and soft voting) has been investigated in detail. RESULTS: The results showed that the classification accuracy was 92% and 99% on the audio-based and PET-based AD datasets, respectively. Our extensive experimental results demonstrate that our PEADD outperforms the state-of-the-art methods on both audio-based and PET-based AD datasets simultaneously. CONCLUSIONS: The network model can provide an objective basis for doctors to detect Alzheimer’s Disease.
Stress-strain state of the plate after impact interaction with an electrode of different shapes
Authors: Sydorenko Yuriy , Ustymenko Pavlo , Pashchyn Mykola , Mikhodui Ol'ha
Published: August 2023

The influence of the shape of the electrode-indentor (cylindrical and parallelepiped with a rounded shape of the contact surface) on the stress-strain state of the plate made of aluminum alloy AMg6 after their impact interaction at a speed of 10m/s was evaluated by the method of numerical modeling. Features of creation and use of developed mathematical models are described. The distribution along the thickness of the plate of the values of the main parameters and components of the stress-strain state, as well as the dimensions and shape of the zone of plastic deformations, the location of zones with compressive and tensile stresses, the depth and width of the dent in the plate, were determined. In particular, it was established that the interaction of an axisymmetric striker with a plate compared to the interaction with a flat striker leads to an increase in the duration of their contact by 20% and, as a result, the dimensions of the dent in the plate increase: the depth by 21%, and the width by 23 %. It was also established that such an interaction leads to an increase in the size of the plastic deformation zone in the plate by almost two times, with its exit beyond the overall dimensions of the striker with a simultaneous transformation of its shape from trapezoidal to rectangular; occurrence in the plate of almost 2 times higher maximum values of effective plastic deformations, which are formed mainly on the contact surface.As a result of the simulation, it was shown that the dynamic processing of a plate with a cylindrical (axisymmetric) indentor with a rounded head, in comparison with a flat elongated shape, leads to the formation of an almost uniform distribution of both components of the stress state (axial and radial) as compressive stresses, which make it possible to combat residual stresses in the material, such as those arising after welding, and this fight against the axial stress component will be less effective (almost 4 times) compared to the fight against the radial component.
Influence of stiffness parameters on vibro-impact damper dynamics
Authors: Lizunov Petro , Pogorelova Olha , Postnikova Tetyana
Published: August 2023

The article studies the dynamic behavior of a low-mass vibro-impact damper, considered as a device for passive vibration control. Its design scheme corresponds to the scheme of single-sided vibro-impact nonlinear energy sink (SSVI NES), which is supposed to be used for effective vibrations attenuation under different transient loads, namely, impulsive, broadband, wind. Its dynamics and effectiveness strongly depend both on the damper own parameters and the external load parameters. We consider the response regimes and the damper efficiency for two options of its optimized parameters under periodic loading. The influence of the elasticity characteristics of the colliding surfaces on the damper effectiveness is also analyzed. We show that the modes with rich complex dynamics are implemented in a system with a heavier damper with low stiffness. Despite this, it is more effective, especially with a softer impact.
Transmission shafts vibrations in transient rotating modes
Authors: Lizunov Petro , Nedin Valentyn
Published: August 2023

The paper presents the investigation results of the transmission shaft dynamic behavior in transient modes of motion with change of the rotational speeds. Such modes occur during the transmission shaft transmits torque from engine to executive device. This process can be accompanied by vibration with change of frequency and amplitude of shaft oscillation. Therefore, the question of studying the dynamic behavior of such systems with identifying the impact of rotational speeds changing on them is relevant. In this regard, the study was done by developed software, in which a technique of computer simulation of the oscillating motion of considerable rotating rods under the action of inertia forces is implemented. Such software gives the possibility to model the oscillatory motion of rotating rods and determine the parameters by which the dynamic stability loss of the studying system can occur. Using this software, the diagrams of rod oscillating motion of the rotating shaft were drawn for definite parameters of the considered system. The process of oscillation is considered in space. The mathematical model of transverse oscillations is described by system of differential equations in rotating coordinate system that is tied to the shaft, but diagrams of oscillations is shown in inertial coordinate system. It is shown that when the speed of rotation changes, namely at the time interval of its increase, this process continues with growth of oscillation frequency during the acceleration time. Also shown that the amplitude of oscillations increases, too. After pass to next constant speed of rotation, the frequency of oscillations, as shown in diagrams, decreases back. Such increase of oscillation frequency during the acceleration can lead to undesirable consequences of destructive nature.
Approaches to ensuring resistance to the progressive collapse of buildings and structures. Current state and prospects
Authors: Vabishchevich Maksym , Fesun Ihor
Published: August 2023

The article provides an overview of accepted approaches to ensuring resistance to progress, in particular the collapse of buildings and structures in the world and in Ukraine, attention is paid to the normative provision of resistance to the progress of collapse. Research on the progress of the collapse of buildings and structures today is a new challenge for ensuring the safety of people's lives. A huge number of buildings and structures destroyed as a result of hostilities were initially only locally damaged, and their instability to the progression of the collapse caused a chain reaction that resulted in complete destruction. The study of this phenomenon will enable the project to support new buildings and structures, as well as to ensure the safe operation of existing ones by ensuring resistance to progressive collapse.
The research of the fire resistance limits of a reinforced concrete slab according to the results of fire tests without mechanical load
Authors: Nuianzin Oleksandr , Kozak Andriy , Kostenko Viktor , Kryshtal Mykola , Nuianzin Vitaliy , Nekora Ol'ha
Published: August 2023

The paper analyzes the thermal effect of fire on reinforced concrete slabs based on their heating according to the standard fire temperature regime in a small-sized installation for the study of the thermal effect of fire on building structures. On the basis of the proposed method, a methodology was created that makes it possible to obtain data on temperature distributions on the surfaces of a slab fragment and in its cross-section. The course and results of the conducted fire test are described, and the adequacy and reproducibility of the experimental data are verified. The temperature distribution over the entire area of the fire furnace, the studied fragments, was analyzed and the obtained results were processed.
The possibility of creating a standard fire temperature regime in the fire furnace chamber has been verified.
According to the results of this work, it was established that during the heating of reinforced concrete, the release of moisture and steam inside the material was observed: on the heated surface, this process took place from 15 to 25 minutes, on the unheated surface from 39 to 57 minutes, and at the level of the reinforcement from 31 to 55 minutes, the heating of the structure at all levels was uniform in the planes of placement of thermocouples, the maximum temperature at the level of the reinforcement was 200°C, it was observed in the last minute of the experiment and continued to increase linearly after leaving the plateau, the maximum temperature on the unheated surface of the rock was 110°C, it observed at the last minute of the experiment and continued to grow linearly after leaving the plateau.
The adequacy of the experimental data was confirmed: the relative deviation did not exceed 4%, and the calculated adequacy criteria (Fisher, Student and Cochrane) were below the critical value.
Numerical and experimental modeling of the behavior of flexible shell elements of structures
Authors: Dzyuba Anatoliy , Safronova Inha , Levitina Larysa
Published: August 2023

This article is devoted to computer and experimental modeling of the behavior of flexible elements of shell structures, and the development of effective algorithms for solving emerging nonlinear boundary value problems of their calculation and optimization of parameters. At the same time, the probability of the results obtained using different approaches to the construction of a nonlinear theory is established. Their comparative analysis, error estimation, which in this case is given by calculation according to linear and corresponding nonlinear theories, is carried out. The results of calculated data and experimental studies of the behavior of real structural elements are compared.

The results of a comparative analysis of the application of two mathematical models of deformation of flexible shell elements, obtained by direct integration of boundary value problems of shell mechanics, by the finite element method and experimental research, are presented.

The problems of axisymmetric nonlinear bending of thin ring plates, corrugated membranes of a sinusoidal profile and bellows as a shell of rotation with a complex meridian shape are considered.

Using the necessary optimality conditions of the principle of maximum L. S. Pontryagin obtained the results of the optimal design of a flexible corrugated membrane with a sinusoidal profile of the highest sensitivity. The results are presented in the form of tables, photos and graphs.
Dynamic stability of a hemispherical shell with shape imperfections
Authors: Lizunov Petro , Lukianchenko Olga , Geraschenko Oleh , Kostina Olena
Published: August 2023

The nonlinear dynamic analysis of imperfect hemispherical shell under pressure was executed. The finite element model of hemisphere in the software NASTRAN was built. The shell wall in the form of the three-cornered and four-cornered finite element net was presented. Shape imperfection as a lower buckling form (Buckling) of perfect hemispherical shell under action of static pressure was modelled. Value of imperfection amplitude was set proportionally to a shell wall thickness. Two boundary conditions in the form rigid and hinged supports on the nodes of lower edge of the shell were considered. Excitation as external pressure, which linearly depended on time and uniform distributed on elements of hemispherical shell was presented. The modal analysis of perfect hemisphere with different wall thicknesses and shell with modelled shape imperfections by the Lanczos method using computational procedure of task on natural vibrations (Normal Modes) was executed. The nonlinear dynamic analysis (Nonlinear Direct Transient) of imperfect hemispherical shell under pressure by N’yumark method was executed. Influence of modelled shape imperfections amplitude on the critical values of dynamic loading and appropriate deformation forms of shell with different boundary conditions were investigated.
It was discovered that a modelled imperfection in the form of a lower buckling form of perfect shell under static pressure in the modal and the dynamic analysis of hemisphere under the same type of the loading was effective. A significant influence of the amplitude of the shape imperfection on the critical values of the dynamic load (55 %) was observed. Also, in our opinion, the presented model of shape imperfection can be used to assess design reliability of the hemisphere under the action of dynamic loads using Bolotin probabilistic approach.
Microbiological Evaluation of Different Types of Branded and Non-branded Ready-to-Eat Snacks Sold in Elementary Schools of District Peshawar, Pakistan
Authors: Hussain Arshad , Rahman Zia Ur , Khan Muddasir
Published: August 2023

The lack of hygiene in the preparation and packing of ready-to-eat foods is fast becoming a serious public health concern, especially for school children. The intervention of different pathogenic microorganisms in these foods poses the risk of foodborne disease outbreaks. The current study was designed to assess bacterial contamination in different types of ready-to-eat (branded and non-branded) snacks purchased from September 2021 to December 2021 in various elementary schools of Peshawar, Pakistan. A total of 20 samples were collected and analyzed using the pour plate method for total plate count (TPC). Moreover, the multiple fermentation tube method was used for total coliforms (TC) and fecal coliforms (FC). Escherichia coli isolates were identified using E. coli O157:H7 latex test reagent kit Pro Lab, Canada. The results revealed contamination by TPC (6.67%), TC (40%), FC (33.33%), and E. coli (20%) in the samples. These values were higher than the permissible limits set by Food and Agriculture Organization (FAO). Hence, it was concluded that there is a practice gap in food safety knowledge among ready-to-eat food vendors. The vendors are usually untrained and lack the knowledge of proper hygiene and food handling procedures. It is suggested here that the government should pay special attention towards improving public awareness regarding food safety and quality of ready-to-eat foods sold in Peshawar, Pakistan.
Association of β-Thalassemia and its Types with ABO and Rh Blood Groups in Lahore, Pakistan
Authors: Waheed Kainat , Rizvi Syed Muhammad Ammad , Mubarak Bushra
Published: August 2023

The current study aims to determine the relationship of ß-thalassemia and its types (major, minor, intermedia) with ABO and Rh blood groups in Lahore, Pakistan. It is an observational study in which ABO and Rh blood grouping of 711 β-thalassemia patients was performed (with their consent). Blood grouping was performed through forward and reverse grouping using tube method. The observed ABO blood group pattern in both genders was O>B>A>AB. It was found that the overall frequency of O and A blood groups was higher in female patients, while the frequency of B and AB blood groups was higher in male patients, although the difference was non-significant. However, a significant difference was observed in the relationship between gender and Rh factor. Most patients of β-thalassemia suffered from the type β-thalassemia major (48%), followed by patients suffering from β-thalassemia minor (44%) and β-thalassemia intermedia (8%). Overall, patients with AB blood group mostly suffered from β-thalassemia major, while patients with β-thalassemia minor and β-thalassemia intermedia had the highest frequency of blood group B. It was concluded that ß-thalassemia patients have a higher frequency of O+ blood group and the lowest frequency of AB- blood group. Moreover, a significant difference was seen between male and female patients with respect to Rh blood group. However, no significant difference (p > 0.05) was found in the distribution of ABO and Rh blood groups corresponding to β-thalassemia types.
KOMUNIKASI POLITIK PEREMPUAN DI KEDIRI
Authors: Orwela Citra
Published: August 2023

The limited number of women involved in political activities, both local and national, is an exciting problem to be examined. Until now, research on women politicians and leadership has not been explored. Starting from these problems, the author wants to go deep into deep research on "Women's Political Communication in Kediri". This study took two informants of female politicians in Kediri. This research is a qualitative type which was conducted by the phenomenological paradigm with an interpretive approach. The results of this study found the importance of communication abilities and skills in the political field.
RESPON BEBERAPA VARIETAS UBI JALAR LOKAL SUMATERA BARAT DENGAN APLIKASI BEBERAPA JENIS PUPUK ORGANIK CAIR
Authors: Sembiring Ngakumalem , Sondang Yun , Anidarfi
Published: August 2023

Ubi jalar termasuk komoditi pangan penghasil karbohidrat penting untuk mendukung ketahanan pangan nasional. Berbagai varietas ubi jalar telah dibudidayakan, namun produktivitas rata-rata masih di bawah varietas unggul. Tujuan penelitian adalah untuk mengetahui tanggap beberapa varietas ubi jalar lokal Sumatera Barat dengan aplikasi beberapa jenis POC. Pelaksanaan penelitian dari April sampai dengan Agustus 2019, berloksi  di lahan Politani Payakumbuh. Perlakuan terdiri dari 2 faktor, yang disusun dalam Rancangan Acak Kelompok faktorial.  Faktor I adalah 4 varietas lokal ubi jalar Sumatera Barat yang dikelompokkan berdasarkan warna daging umbi, yaitu V1=  umbi ungu muda ; V2 = umbi ungu tua; V3 = umbi oranye; dan V4 =   umbi putih dan faktor ke II adalah 3 pelakuan POC dan 1 kontrol, yaitu P0 = tanpa POC; P1 = POC MOL Buah; P2 = POC Sabut Kelapa; dan P3 = POC pabrikan, sehingga diperoleh 16 unit perlakuan dengan 3 kali ulangan. Hasil penelitian menunjukkan bahwa perlakuan varietas berpengaruh nyata terhadap pertumbuhan vegetatif (panjang batang dan jumlah cabang) sedangkan perlakuan POC tidak memberikan pengaruh nyata.  Perlakuan varietas dan POC berpengaruh nyata terhadap pertumbuhan generatif.  Perlakuan MOL buah memberikan pengaruh nyata dan meningkatkan bobot umbi/tanaman, berbeda tidak nyata dengan POC sabut kelapa dan POC pabrikan. Sedangkan perlakuan varietas memberikan pengaruh nyata terhadap jumlah dan bobot umbi/tanaman. Jumlah dan bobot umbi tertinggi terdapat pada varietas ubi jalar ungu tua, diikuti oleh ubi jalar putih, ubi jalar oranye, dan terendah pada ubi jalar ungu muda.
UJICOBA EFEKTIFITAS KOMPOSTER SEDERHANA BERBASIS MAGGOT BLACK SOLDIER FLY (BSF) SEBAGAI SOLUSI PENGELOLAAN SAMPAH DAPUR SKALA RUMAH TANGGA
Authors: Afner Synthia Ona Guserike , Karo Muliadi Karo , Zudri Fatardho
Published: August 2023

Sampah-sampah organik yang dihasilkan dalam skala rumah tangga biasanya hanya akan ditumpuk atau dikirimkan ke Pengolahan Sampah Akhir yang akan menjadi sumber bau dan polusi udara. Padahal sampah organik sisa dapur ini memiliki potensi yang baik jika diolah menjadi kompos. Komposisi sumber sampah di Indonesia adalah sampah organik merupakan komponen terbesar, mencapai diatas 70%, dimana sampah dapur atau sisa makanan mencapai 20-65% tergantung kelas ekonomi masyarakatnya. Maggot BSF memiliki potensi yang besar dalam mengolah berbagai macam jenis sampah, mulai dari sampah pasar hingga sampah rumah tangga dengan waktu yang relative singkat. Hasil dari pengolahan menggunakan larva lalat BSF menghasilkan kompos yang lebih baik daripada pupuk kandang maupun olahan sampah sisa tanaman. Penelitian ini bertujun untuk merancang komponen komposter sederhana yang beroperasi secara semi otomatis dalam mengolah sampah rumah tangga, dengan memanfaatkan maggot sebagai agen utamanya dan menguji efektifitas komposter sederhana, sehingga menjadi solusi bagi pengolahan sampah organik rumah tangga. Tahapan penelitian dimulai dari perakitan komposter, pengisian media sisa sampah organik rumah tangga, pengamatan aktifitas maggot, analisa kualitas kompos. Berdasarkan hasil pengamatan terhadap komposter terlihat komponen berfungsi secara efektif dibuktikan dari jumlah pre pupa yang tertampung wadah migrasi yang disediakan. Berdasarkan hasil pengamatan kompos terlihat bahwa maggot sudah secara efektif mengolah sampah dapur, ditunjukkan dari warna sudah berubah menjadi hitam dan tidak berbau, namun masih terlihat basah. Berdasarkan hasil analisa kompos didapatkan kadar air 40,33%, pH 6,8, Nitrogen 0,61%, Karbon 27,05%, Phospor 0,16%, Kalium 0,8%, Kalsium, 1,51%, Besi 0,0038 %. Hasil analisa ini sebagian besar sudah sesuai dengan SNI kualitas kompos. Berdasarkan hasil yang didapatkan dapat disimpulkan bahwa komposter sederhana sudah dapat bekerja dengan baik dalam mengolah sampah dapur dan menjaga siklus maggot Black Soldier Fly (BSF) dapat berjalan dengan baik.
Importância da Linfadenectomia Axilar no Tratamento Cirúrgico do Câncer da Mama
Authors: Brondi Luiz Antonio Guimarães
Published: August 2023

Não existem dúvidas quanto à importância dos linfonodos axilares no prognóstico e tratamento do câncer da mama. Em 151 mastectomias realizadas, empregando-se as técnicas de Halsted-Meyer (104 casos) e de Patey-Dyson (47 casos), encontrou-se um total de 5.015 linfonodos axilares, com a média de 33,2 por axila. Não houve diferença significativa entre o número médio de linfonodos encontrados na axila, tanto com uma quanto com outra técnica de mastectomia. Os linfonodos foram divididos de acordo com os níveis de drenagem axilar e de acordo com os seus maiores diâmetros. Observou-se uma estreita correlação entre o diâmetro de cada um deles e o seu comprometimento histológico, de onde se conclui que esse parâmetro pode ser considerado como outro indicador prognóstico para o câncer da mama. Os pequenos linfonodos (0,5cm ou menos) estavam presentes em todos os níveis, ao contrário do que ocorreu com aqueles maiores que 2cm de diâmetro, encontrados quase exclusivamente no primeiro nível de drenagem axilar. Por outro lado, estes últimos apresentaram uma taxa de comprometimento histológico mais elevada que os demais. Observou-se uma taxa de falso negativo de 22,5%, a qual foi mais elevada entre os casos mais iniciais de estadiamento da moléstia. O comportamento histológico da axila só poderá ser comprovado se todos os linfonodos encontrados forem examinados, o que será possível após um bom esvaziamento axilar, incluindo a retirada do músculo peitoral menor. Dessa maneira, cirurgias mais conservadoras, com preservação dos músculos peitorais, apenas levarão a uma linfadenectomia axilar incompleta. Foi muito raro o comprometimento de linfonodos situados em níveis superiores sem que o primeiro nível estivesse comprometido. Assim como foi raro encontrar linfonodos pequenos comprometidos sem que os maiores estivessem histologicamente positivos. Foi descrito um grupo de linfonodos, considerado uma das cadeias mais altas da axila, denominado GRUPO PARACEFÁLICO, encontrado em 34 mastectomias (22,5%). Os linfonodos menores que 0,5cm de diâmetro, clinicamente não detectáveis, mas histologicamente com prometidos, foram denominados METASTASES AXILARES MINIMAS DA MAMA.
Capturing the Personal and Pedagogical Experiences of Faculty During Emergency Response Teaching at a Research University in Abu Dhabi
Authors: Dalton David , Hassan Asli , Mejri Sami B , Omer Amani
Published: August 2023

The purpose of this qualitative study was to examine the teaching experiences and emotional well-being of faculty at a research university in the United Arab Emirates (UAE). The study took place during Emergency Response Teaching due to the global pandemic and its implications on instruction and learning. A 60-question-line interview questionnaire was disseminated to 400 faculty members during the spring semester of 2020. 26 faculty members (6.5%) completed the interview questionnaire. Results showed evidence of a range of pandemic-related, negative impacts on the faculty’s experiences and feelings. Many faculty respondents were challenged and frustrated in terms of being able to respond to a range of issues and themes identified in the study. The discussion focused on the development of preemptive approaches which might be developed for future use should a comparable situation arise.