Miracle-dz/UniFAR-FTUs huggingface dataset:
The Healthcare Internet of Things as a Paradigm Shift in Hospital Management, Patient Care, and Medical Data Analysis

When it comes to hospital administration, patient care, and medical data analysis, the Healthcare Internet of Things (HIoT) is nothing short of a paradigm revolution. We dive into this new paradigm to examine its far-reaching effects and revolutionary possibilities in the healthcare system. The context is established by introducing HIoT as a game-changing development in healthcare. Using the IoT to network several devices, this model paves the way for real-time patient monitoring, to provide a precise depiction of the healthcare system, dataset environments use well maintained medical data sources. The performance and efficacy of HIoT technologies may be evaluated using measurable criteria such as sensitivity (0.94), specificity (0.89), F1-Score (0.91), ROC-AUC (0.95), and cost savings (∃150,000). To determine the relative importance of each part of the HIoT ecosystem, researchers undertake “ablation studies”. Our findings provide a clear picture of the disruptive potential of HIoT. Better patient outcomes may be ensured via early interventions thanks to the improved accuracy (0.92), efficiency (9.2), and satisfaction (9.2) provided by the suggested HIoT technique for patient monitoring. to its persistent outperformance of conventional methods. With cutting-edge technologies and a hopeful future for both patients and healthcare professionals, HIoT is in the front of healthcare's ongoing revolution.
A Knowledge-Based System of Ethno-Medicinal Plants in Lanao Del Sur for Treating Diseases

This paper presents a comprehensive exploration of the ethno-medicinal knowledge in Lanao del Sur, a region rich in biodiversity and culture. Focusing on the traditional uses of plants for therapeutic purposes, the study integrates indigenous knowledge with modern technology to develop a knowledge-based system for recommending ethno-medicinal plants to treat common diseases. Knowledge had been acquired from local people with expertise in ethnomedicinal practices; that acquired knowledge would be translated into rules that can be read by the rule engine; and after translating all the knowledge into rules, it would be tested for its correctness in recommendation. This interdisciplinary approach contributes to the preservation of traditional knowledge and opens avenues for sustainable healthcare practices that bridge the gap between traditional wisdom and contemporary science. The paper advocates for recognizing and integrating ethno-medicinal practices into mainstream healthcare systems, fostering a harmonious coexistence of cultural heritage and modern advancements in medical science within Lanao del Sur and beyond.
Optimized Transform Entropy Decoding Architecture for VDC-M

The importance of video compression technology is highly emphasized according to the advancement of display technology which plays a crucial role in many industries. Video compression technology is important in the aspect of reducing bandwidth usage, optimizing storage space, and enhancing the efficiency of data transmission. Video Electronics Standards Association (VESA) Display Compression-M (VDC-M) video compression standard [1], as released by the VESA, shows high compression ratio and high performance. In this paper, we propose a hardware architecture for transform entropy decoding which supports the VDC-M standard. It can extract and align quantized coefficients which are transmitted from encoder to decoder for reconstruction. The utilization of optimized entropy coding techniques enhances the overall performance, making the proposed hardware architecture contribute to advanced video compression in modern display technologies.
Vehicle Intelligent Application System Based on Improved YOLOv5 and DeepSORT

Nowadays, the coverage of the expressway is constantly expanding, and the traffic volume on each highway is also rapidly expanding. This research suggests a vehicle analysis system based on YOLOv5+DeepSORT to overcome the poor real-time performance of conventional traffic statistics and low accuracy in detection for original algorithms. In this paper, we propose a method named YOLOv5-Car based on YOLOv5 model, which mAP is 1.6% higher than that of the YOLOv5 model. The YOLOv5 model is improved in 2 ways: (1) When the Coordinate Attention (CoordAtt) module is added to the YOLOv5 backbone network, channel attention is divided into two 1-dimensional feature coding processes and aggregated along two spatial directions, respectively. This method can enhance the position and depiction of interesting objects in the input feature map. (2) Replace the Non Maximum Suppression (NMS) based technique with the more accurate and completely parallelizable Confidence Propagation Cluster (CP-Cluster). The outcomes of the experiments show that the strategy suggested in this study can improve the precision of the vehicle analysis system.
Blood Thixotropy in Patients with Sickle Cell Anaemia: Role of Haematocrit and Red Blood Cell Rheological Properties

We compared the blood thixotropic/shear-thinning properties and the red blood cells’ (RBC) rheological properties between a group of patients with sickle cell anaemia (SS) and healthy individuals (AA). Blood thixotropy was determined by measuring blood viscosity with a capillary viscometer using a “loop” protocol: the shear rate started at 1 s−1 and increased progressively to 922 s−1 and then re-decreased to the initial shear rate. Measurements were performed at native haematocrit for the two groups and at 25% and 40% haematocrit for the AA and SS individuals, respectively. RBC deformability was determined by ektacytometry and RBC aggregation properties by laser backscatter versus time. AA at native haematocrit had higher blood thixotropic index than SS at native haematocrit and AA at 25% haematocrit. At 40% haematocrit, SS had higher blood thixotropic index than AA. While RBC deformability and aggregation were lower in SS than in AA, the strength of RBC aggregates was higher in the former population. Our results showed that 1) anaemia is the main modulator of blood thixtropy and 2) the low RBC deformability and high RBC aggregates strength cause higher blood thixotropy in SS patients than in AA individuals at 40% haematocrit, which could impact blood flow in certain vascular compartments.
An Algorithm to Enumerate All Cutsets of a Graph in Linear Time per Cutset

Thts paper deals wRh the problem of enumerating all the cutsets or all the s-t cutsets separatmg two spectfied verttces s and t m an undirected graph A vanety of approaches have been proposed for this problem, among which one based on the partmon e ra set of veruces rote two sets is the most effi¢ienL It is first shown that an algorithm of this type has time complexity O((n + m)(n log2#)#), and two new algorithms with ume complexity O((n + m)O + I)) are then proposed One of these new algorithms has space complexity O(nZ), and the other has space complexity O(n + m), where n and m are the numbers of veraces and edges, respectively, and ta ts the number ofs-t cutsets m a given graph The results of some computatmnal experiments are also described. An mvest~gaUon ~s made of the extent to whtch the new algorithms are better, and how good the performance of the old algorithm is, especmlly when a given graph is "dense," t e , 2m/(n(n 1)) _> 0.4.
Ultra-Thin LIG Film for X band EMI Shielding Applications

The development of lightweight and ultrathin materials is quite important for electromagnetic interference (EMI) shielding of myriads of electrical devices, especially in the telecommunication and the aerospace industry. In this work, a novel ultrathin Laser Induced Graphene (LIG) film with total thickness of only 0.05 mm is synthesized, fabricated and tested for the EMI shielding application. The proposed LIG film, based on the customized conductive material, is fabricated using a pulsed CO2 laser system. It is demonstrated that the fabricated LIG sample exhibits more than 10 dB total shielding effectiveness (SET) in the entire X band. The ratio of SET/ thickness(mm) for the fabricated LIG film is found to be greater than 200, which is phenomenally high as compared to the state of the art EMI shielding materials. Further, the electrical conductivity of the LIG film is measured and this data is utilized to perform simulation using the numerical EM simulator; the CST Microwave Studio for the SET performances. A close matching is observed between the measured and the simulated SE data. The proposed synthesis scheme appears to be quite promising for design and development of lightweight EMI shielding materials, which can have wide range of applications in industry.
A 30–60 GHz SiGe transformer balun with offset radii coils for low amplitude and phase imbalance

Characterization of a wide-band transformer balun with low magnitude and phase imbalance is presented in this paper. Excellent balance over a large bandwidth is achieved by adopting two new techniques for the transformer balun design, resulting in a very low magnitude imbalance of 0.12 dB and phase imbalance of less than 1° over 30 to 60 GHz. The tradeoff between the insertion loss and the balance of the balun is investigated. First, the appropriate width of the primary and the secondary coils is selected for a reasonable insertion loss. Secondly, the radii of the primary and secondary coils were offset to reduce the parasitic coupling capacitance, thereby improving the balance of the differential signal. The balun is fabricated in 0.13 μιη SiGe technology. The balun is very compact with chip size of 0.2 mm × 0.145 mm.
Ontology-Based Conversational Recommender System for Motorcycle

It is common for customers to face challenges when trying to choose a vehicle that fits their modern lifestyle. Even though there are many recommender systems available to assist with making informed decisions based on unique needs, these systems often lack direct user involvement. Additionally, their recommendations are primarily based on technical specifications rather than functional requirements. To address these limitations, a recent study aimed to create an ontology-based conversational recommender system. This system incorporates user preferences and offers personalized recommendations based on functional requirements. The study evaluated the system based on accuracy and user satisfaction metrics and found that it achieved an impressive recommendation accuracy rate of 87.84%. Furthermore, the study received positive feedback from users searching for motorcycles based on various functional requirements. This feedback is a testament to the system's effectiveness in aiding customers in making informed decisions.
Integrating Blockchain Technology with PKI for Secure and Interoperable Communication in 5G and Beyond Vehicular Networks

Security and privacy are crucial in V2X networks due to sensitive user information. Public Key Infrastructure (PKI) is widely used in C-ITS to ensure security and privacy. However, the practical implementation of PKI faces challenges in achieving seamless communication across diverse ITS projects worldwide. The absence of interoperability between PKI systems and unre-solved issues in existing PKI standards hinder global adoption. Despite available security standardizations using centralized PKI technology for V2X, a universally adopted PKI-based security architecture is necessary. Furthermore, the progress made in 5G V2X technology has demonstrated significant potential for revolutionizing V2X communication in the future. Enhancing the level of trust through integration of the 5G Core Network (5GC) into the PKI security mechanism can lead to more secure and efficient V2X communication. To address these challenges, we propose a blockchain-based architecture that integrates the 5GC network with the PKI infrastructure, aiming to enhance privacy and security in 5G V2X communication. Our solution is designed to be distributed and interoperable, aligned with existing ETSI ITS PKI standard. By utilizing Hyperledger Fabric (HLF) platform, a permissioned blockchain framework, we present the architecture and conduct a comprehensive security analysis to ensure compliance with security and privacy requirements of V2X communications.
Applying Machine Learning Algorithms to Investigate Cervical Cancer

Cervical cancer is a significant health challenge globally, particularly in regions with limited access to healthcare resources. Timely and accurate classification of cervical cancer risk is crucial for effective interventions and personalized treatment plans. The paper provides an overview of preliminary results in cervical cancer risk classification, with a focus on three machine learning algorithms namely Support Vector Machines, Capsule CNN and CNN. Support Vector Machine (SVM), Capsule Network, and Convolution Neural Network have emerged as promising methods to predict cervical cancer risk based on various risk factors and biomarkers. These algorithms analyze the available datasets on Cervical Cancer from Kaggle and identify complex patterns, demonstrating potential risk in improving cancer detection accuracy. In addition, we emphasize the importance of prevention measures, including HPV vaccination and regular screenings, to combat cervical cancer effectively. By combining innovative classification algorithms with preventive efforts, we can enhance cervical cancer management and improve patient outcomes worldwide.
Impossible Shaded Images

It is shown that shaded images that cannot have originated from a uniformly illuminated, smooth continuous surface with uniform albedo exist. The typical condition where this occurs is when a dark area (corresponding to a region of high gradient) is surrounded by a lighter region (with low gradient). For this to correspond to a real surface, it must be established that there is a local extremum or area of lower gradient inside the dark region. This, in turn, will show up as either a light area in the image or an orientation discontinuity in the surface (thus violating either intensity or smoothness constraints). The impossibility of a shaded image can be established by counting the number of extrema inside a region corresponding to an isolated surface patch. >
Examining the Influence of Trimodal Multisensory Stimuli on Presence, Perceived Realism, and Quality of Experience in Video Visualization

We experience the world around us using all our senses, however, multimedia content still relies majorly on audiovisual stimuli. With technology advancements, multisensory stimuli started to be introduced in multimedia experiences. Still, very few contemplate a wide range of different modalities simultaneously, approaching the stimulation one would receive in reality. This paper explores the effects of trimodal multisensory stimuli on the sense of Presence, Perceptual Realism, and Quality of Experience (QoE) during video visualisation. Namely, we study the impact of heat, wind, and smell during video visualization to investigate how each stimulus contributes to the QoE. A correlational analysis was also performed to understand better how the different variables interact. The results indicate that multisensory stimulation improved significantly the sense of presence satisfaction and perceptual realism. Furthermore, smell contributed the most to the QoE, followed by heat and wind. We highlight the use of multisensory stimulation on video visualization over audiovisual only, as it benefits substantially the user experience.
Development of LTCC based Inductors with Barium Hexaferrite Nanoparticles

In this paper fabrication and characterization of ferrite-based inductors using LTCC technology is presented. Barium Hexaferrite (BaFe12O19) in the form of a paste is applied in between LTCC layers and cofired to form a ferrite-embedded inductor (2 turns) in design 1. In design 2, LTCC-compatible ferrite paste is developed in the laboratory and screen printed on the top layer to fabricate the inductor. These inductors are characterized in the frequency range of 1 GHz to 4 GHz. Design 1 shows a peak inductance of 11.51nH at 2.233 GHz and Quality factor(Q) 31. Design 2 shows a peak inductance of 8.795nH at 2.979 GHz and Q of 15.68 at this frequency.
A Novel Approach to Enhance the Performance of TFET using Metal Strip Layer for High-Frequency Applications

In this paper, a novel approach to enhance the electrical characteristics of the tunnel field effect transistor (TFET) by introducing optimized metal strips (referred to as MS) into the oxide layer in both the drain-channel (D-C) and source-channel (S-C) regions are presented. This modification has led to the creation of a new device called the metal-strip tunnel FET (MS-TFET). The simulation results have demonstrated significant improvements in the ON-state current, point subthreshold slope (SS) of approximately 23.2 mV/decade, and improvements in analog/radio frequency (RF) figure of merit (FOMs). The proposed device exhibits great potential for applications in high-frequency and analog circuits, particularly in scenarios where low gate voltages are required for ultra-low power and high-frequency circuit applications. All simulations are performed using 2-D ATLAS software.
In-Situ Leak Detection in Urban Water Supply Pipelines Using Flexible Vibration Sensors

The urban water supply pipeline network is an important infrastructure for the survival and development of cities, and its leak problem has long lacked efficient detection methods. Existing leak detection methods mainly have limitations in micro-leakage detection and large-scale deployment and are susceptible to environmental interference. This work presents an urban water supply pipeline leak detection method based on flexible piezoelectric sensors. The flexibility of the thin film sensor allows it to be directly attached to curved pipe walls, enabling in-situ monitoring of local leakages. The acquired vibration leakage signals are further processed, and a support vector machine (SVM) algorithm is used to classify leakage conditions to various levels with a high accuracy of more than 95%.
The German agricultural soil carbon inventory: conceptual framework and methodology

Soil organic matter is a key parameter for soil quality, productivity and soil resilience to erosion and compaction. Agricultural soils tend to show depletion in soil organic carbon stocks due to cultivation. Intensive farming and almost complete biomass removal by harvest bring the input-output equilibrium of soil C out of alignment. Climate change increases the risk of soil organic matter losses by enhanced mineralisation so that the paradigm that soil organic carbon stocks are in equilibrium with their long-term use may not longer be valid.
METHODOLOGICAL FIT IN MANAGEMENT FIELD RESEARCH

Methodological fit, an implicitly valued attribute of high-quality field research in organizations, has received little attention in the management literature. Fit refers to internal consistency among elements of a research project—research question, prior work, research design, and theoretical contribution. We introduce a contingency framework that relates prior work to the design of a research project, paying particular attention to the question of when to mix qualitative and quantitative data in a single research paper. We discuss implications of the framework for educating new field researchers.
Intelligent Regression Load Forecasting for Non-Uniform Urban Load

A well-studied load forecast is usually perceived as an essential parameter for the cost control of any utility. Not only, but also, load forecasting has a great impact on creating an energy profile for utilities. The energy profile helps to assume the amount of renewable energy interference needed to sustain the electricity consumption from conventional generation. Maintaining a balanced and economic profile of an urban consuming utility can be very challenging when considering any external influencing factors. This study finds the optimum forecast using artificial intelligence for a commercial mall load data in the state of Arizona among different regression models within linear regression models, regression trees, Gaussian process regression models, support vector machines and ensembles of regression trees models to train the data supplied. According to the model used in this paper, the study provides forecasts along with their root mean squared errors in medium-term load forecasting, considering temperature, humidity, time, and seasonal data. This study compares forecasting errors after clustering a yearly hourly load profile into a summer season with three parts of the day. The results states that fine regression trees, quadratic support vector machines and rational gaussian process regression models resulted in the least root mean square errors for load forecasting.
Impaired Cerebral Vasoreactivity in a Transgenic Mouse Model of Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy Arteriopathy

Background and Purpose— Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an inherited small vessel disease causing stroke and dementia. The disease is caused by highly stereotyped mutations in NOTCH3, which is restrictively expressed in vascular smooth muscle cells (VSMCs). The mechanisms of compromised cerebral hemodynamics in CADASIL remain to be elucidated. We tested the hypothesis that mutant NOTCH3 impairs the vasomotor function of cerebral vessels. Methods— Vasomotor function was examined in vivo in transgenic mice expressing a mutant NOTCH3 in VSMCs (TgNotch3R90C). Mice develop an age-dependent arteriopathy similar to that seen in CADASIL, without brain parenchyma lesions. Using laser-Doppler flowmetry, we assessed in awake TgNotch3R90C mice and wild-type littermates the cerebrovascular reactivity to 2 potent vasodilator stimuli (acetazolamide and hypercapnia) and cerebral blood flow (CBF) autoregulation during stepwise blood pressure elevations and reductions. Mice were studied at 18 months of age, when the CADASIL features are apparent, and at 10 months of age, before their appearance. Results— Eighteen-month-old TgNotch3R90C mice showed reduced responses to hypercapnia and acetazolamide, higher cerebrovascular resistance during hypertension, and their lower limit of CBF autoregulation was shifted to higher blood pressures. Cerebrovascular responses were similarly impaired in 10-month-old TgNotch3R90C mice. Conclusions— Cerebrovascular reactivity is compromised early in TgNotch3R90C mice. The data show an impaired autoregulation and are suggestive of a decreased relaxation or increased resistance of cerebral vessels. Our findings indicate that vascular dysfunction is an early pathogenic event that may promote the subsequent development of brain ischemia in CADASIL.
Reasoning about Procedure Calls with Repeated Arguments and the Reference-Value Distinction

2002 CR Categories: D.2. 1 [Software Engineering] Requirements/Specifications languages; D.2. 4 [Software Engineering] Software/Program Verification correctness proofs, formal methods, programming by contract, reliability; D.3. 1 [Programming Languages] Formal Definitions and Theory semantics; D.3. 3 [Programming Languages] Language Constructs and Features Procedures, functions, and subroutines; F.3. 1 [Logics and Meanings of Programs] Specifying and Verifying and Reasoning about Programs Assertions, logics of programs, preand postconditions, specification techniques; F.3. 3 [Logics and Meanings of Programs] Studies of Program Constructs Functional constructs, object-oriented constructs.