bluuebunny/crossref_metadata_2025 huggingface dataset:
Semi-symbolic inference for efficient streaming probabilistic programming
Authors: Atkinson Eric , Yuan Charles , Baudart Guillaume , Mandel Louis , Carbin Michael
Published: October 2022

A streaming probabilistic program receives a stream of observations and produces a stream of distributions that are conditioned on these observations. Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filters (RBPFs), which exactly solve inference problems when possible and fall back on sampling approximations when necessary. While RBPFs can be implemented by hand to provide efficient inference, the goal of streaming probabilistic programming is to automatically generate such efficient inference implementations given input probabilistic programs.

In this work, we propose semi-symbolic inference, a technique for executing probabilistic programs using a runtime inference system that automatically implements Rao-Blackwellized particle filtering. To perform exact and approximate inference together, the semi-symbolic inference system manipulates symbolic distributions to perform exact inference when possible and falls back on approximate sampling when necessary. This approach enables the system to implement the same RBPF a developer would write by hand. To ensure this, we identify closed families of distributions – such as linear-Gaussian and finite discrete models – on which the inference system guarantees exact inference. We have implemented the runtime inference system in the ProbZelus streaming probabilistic programming language. Despite an average 1.6× slowdown compared to the state of the art on existing benchmarks, our evaluation shows that speedups of 3×-87× are obtainable on a new set of challenging benchmarks we have designed to exploit closed families.
Synthesizing axiomatizations using logic learning
Authors: Krogmeier Paul , Lin Zhengyao , Murali Adithya , Madhusudan P.
Published: October 2022

Axioms and inference rules form the foundation of deductive systems
and are crucial in the study of reasoning with logics over structures.
Historically, axiomatizations have been discovered manually with much
expertise and effort. In this paper we show the feasibility of using synthesis
techniques to discover axiomatizations for different classes of
structures, and in some contexts, automatically prove their
completeness. For evaluation, we apply our technique to find axioms
for (1) classes of frames in modal logic characterized in first-order
logic and (2) the class of language models with regular operations.
Synthesizing code quality rules from examples
Authors: Garg Pranav , Sengamedu Srinivasan H.
Published: October 2022

Static Analysis tools have rules for several code quality issues and these rules are created by experts manually. In this paper, we address the problem of automatic synthesis of code quality rules from examples. We formulate the rule synthesis problem as synthesizing first order logic formulas over graph representations of code. We present a new synthesis algorithm RhoSynth that is based on Integer Linear Programming-based graph alignment for identifying code elements of interest to the rule. We bootstrap RhoSynth by leveraging code changes made by developers as the source of positive and negative examples. We also address rule refinement in which the rules are incrementally improved with additional user-provided examples. We validate RhoSynth by synthesizing more than 30 Java code quality rules. These rules have been deployed as part of Amazon CodeGuru Reviewer and their precision exceeds 75% based on developer feedback collected during live code-reviews within Amazon. Through comparisons with recent baselines, we show that current state-of-the-art program synthesis approaches are unable to synthesize most of these rules.
Modular verification of op-based CRDTs in separation logic
Authors: Nieto Abel , Gondelman Léon , Reynaud Alban , Timany Amin , Birkedal Lars
Published: October 2022

Operation-based Conflict-free Replicated Data Types (op-based CRDTs) are a family of distributed data structures where all operations are designed to commute, so that replica states eventually converge. Additionally, op-based CRDTs require that operations be propagated between replicas in causal order. This paper presents a framework for verifying safety properties of CRDT implementations using separation logic. The framework consists of two libraries. One implements a Reliable Causal Broadcast (RCB) protocol so that replicas can exchange messages in causal order. A second “OpLib” library then uses RCB to simplify the creation and correctness proofs of op-based CRDTs. OpLib allows clients to implement new CRDTs as purely-functional data structures, without having to reason about network operations, concurrency control and mutable state, and without having to each time re-implement causal broadcast. Using OpLib, we have implemented 12 example CRDTs from the literature, including multiple versions of replicated registers and sets, two CRDT combinators for products and maps, and two example use cases of the map combinator. Our proofs are conducted in the Aneris distributed separation logic and are formalized in Coq. Our technique is the first work on verification of op-based CRDTs that satisfies both of the following properties: it is
modular
and targets executable
implementations
, as opposed to high-level protocols.
Implementing and verifying release-acquire transactional memory in C11
Authors: Dalvandi Sadegh , Dongol Brijesh
Published: October 2022

Transactional memory (TM) is an intensively studied synchronisation paradigm with many proposed implementations in software and hardware, and combinations thereof. However, TM under relaxed memory, e.g., C11 (the 2011 C/C++ standard) is still poorly understood, lacking rigorous foundations that support verifiable implementations. This paper addresses this gap by developing TMS2-ra, a relaxed operational TM specification. We integrate TMS2-ra with RC11 (the repaired C11 memory model that disallows load-buffering) to provide a formal semantics for TM libraries and their clients. We develop a logic, TARO, for verifying client programs that use TMS2-ra for synchronisation. We also show how TMS2-ra can be implemented by a C11 library, TML-ra, that uses relaxed and release-acquire atomics, yet guarantees the synchronisation properties required by TMS2-ra. We benchmark TML-ra and show that it outperforms its sequentially consistent counterpart in the STAMP benchmarks. Finally, we use a simulation-based verification technique to prove correctness of TML-ra. Our entire development is supported by the Isabelle/HOL proof assistant.
Fast shadow execution for debugging numerical errors using error free transformations
Authors: Chowdhary Sangeeta , Nagarakatte Santosh
Published: October 2022

This paper proposes, EFTSanitizer, a fast shadow execution framework for detecting and debugging numerical errors during late stages of testing especially for long-running applications. Any shadow execution framework needs an oracle to compare against the floating point (FP) execution. This paper makes a case for using error free transformations, which is a sequence of operations to compute the error of a primitive operation with existing hardware supported FP operations, as an oracle for shadow execution. Although the error of a single correctly rounded FP operation is bounded, the accumulation of errors across operations can result in exceptions, slow convergences, and even crashes. To ease the job of debugging such errors, EFTSanitizer provides a directed acyclic graph (DAG) that highlights the propagation of errors, which results in exceptions or crashes. Unlike prior work, DAGs produced by EFTSanitizer include operations that span various function calls while keeping the memory usage bounded. To enable the use of such shadow execution tools with long-running applications, EFTSanitizer also supports starting the shadow execution at an arbitrary point in the dynamic execution, which we call selective shadow execution. EFTSanitizer is an order of magnitude faster than prior state-of-art shadow execution tools such as FPSanitizer and Herbgrind. We have discovered new numerical errors and debugged them using EFTSanitizer.
Advances and Applications of Polygenic Scores for Coronary Artery Disease
Authors: Patel Aniruddh P. , Khera Amit V.
Published: October 2022

Polygenic scores quantify inherited risk by integrating information from many common sites of DNA variation into a single number. Rapid increases in the scale of genetic association studies and new statistical algorithms have enabled development of polygenic scores that meaningfully measure—as early as birth—risk of coronary artery disease. These newer-generation polygenic scores identify up to 8% of the population with triple the normal risk based on genetic variation alone, and these individuals cannot be identified on the basis of family history or clinical risk factors alone. For those identified with increased genetic risk, evidence supports risk reduction with at least two interventions, adherence to a healthy lifestyle and cholesterol-lowering therapies, that can substantially reduce risk. Alongside considerable enthusiasm for the potential of polygenic risk estimation to enable a new era of preventive clinical medicine is recognition of a need for ongoing research into how best to ensure equitable performance across diverse ancestries, how and in whom to assess the scores in clinical practice, as well as randomized trials to confirm clinical utility.
Multiresolution Mutual Assistance Network for Cardiac Magnetic Resonance Images Segmentation
Authors: Chen Shaolong , Qiu Changzhen , Yang Weiping , Zhang Zhiyong
Published: October 2022

The automatic segmentation of cardiac magnetic resonance (MR) images is the basis for the diagnosis of cardiac-related diseases. However, the segmentation of cardiac MR images is a challenging task due to the inhomogeneity of MR images intensity distribution and the unclear boundaries between adjacent tissues. In this paper, we propose a novel multiresolution mutual assistance network (MMA-Net) for cardiac MR images segmentation. It is mainly composed of multibranch input module, multiresolution mutual assistance module, and multilabel deep supervision. First, the multibranch input module helps the network to extract local and global features more pertinently. Then, the multiresolution mutual assistance module implements multiresolution feature interaction and progressively improves semantic features to more completely express the information of the tissue. Finally, the multilabel deep supervision is proposed to generate the final segmentation map. We compare with state-of-the-art medical image segmentation methods on the medical image computing and computer-assisted intervention (MICCAI) automated cardiac diagnosis challenge datasets and the MICCAI atrial segmentation challenge datasets. The mean dice scores of our method in the left atrium, right ventricle, myocardium, and left ventricle are 0.919, 0.920, 0.881, and 0.960, respectively. The analysis of evaluation indicators and segmentation results shows that our method achieves the best performance in cardiac magnetic resonance images segmentation.
Cetuximab Responses in Patients with HNSCC Correlate to Clonal Expansion Feature of Peripheral and Tumor-Infiltrating T Cells with Top T-Cell Receptor Clonotypes
Authors: Ge Huaibin , Ferris Robert L. , Wang Jing H.
Published: October 2022

Purpose:

Cetuximab is a standard-of-care treatment for head and neck squamous cell carcinoma (HNSCC). Well-defined correlative markers of therapeutic responses are still lacking. Characterizing dynamic changes of T-cell receptor (TCR) repertoire in peripheral blood and tumor tissue may facilitate developing markers for cetuximab response in HNSCCs.

Experimental Design:

We analyzed high-throughput TCRβ sequencing data generated with ImmunoSEQ platform using peripheral blood mononuclear cells (PBMC) and tumor-infiltrating lymphocytes (TIL) from patients with HNSCC before and after cetuximab treatment (pre-/post-PBMC vs. pre-/post-TIL). Multiple analytic approaches were employed to normalize sequencing data.

Results:

Normalized TCR richness was significantly lower in post-TIL than pre-TIL, suggesting that cetuximab reduced TCR diversity and promoted TCR expansion in TIL samples, regardless of response status. The magnitude of clonal expansion (defined as expansion rate) in top 20 TCR clonotypes was significantly higher in responder PBMC with or without normalization, and in responder TIL upon normalization, than nonresponder ones. Notably, the expanded top 20 or top 50 TCR clonotypes overlapped between PBMC and TIL samples, which occurred significantly more frequently in responders than nonresponders.

Conclusions:

Patients with cetuximab-treated HNSCC harbor dynamic changes of TCR repertoires correlative to therapeutic responses. The expansion rate of top TCR clonotypes in peripheral blood may serve as a minimally invasive, readily accessible, and feasible marker for predicting cetuximab responses in HNSCCs and beyond, and the expansion rate of top TCR clonotypes in TILs and their overlapping probability between PBMC and TIL may serve as additional predictive markers. Our study also highlights the importance of data normalization for TCR repertoire analysis.
Severe Hypercholesterolemia Mediated by Lipoprotein X in an Immunosuppressed Patient: A Case Report
Authors: Sequeira Cristiana , Coelho Mariana , Costa Santos Inês , Ramos Lopes Sara , Mangualde João , Oliveira Ana Paula
Published: October 2022

Cholestatic liver diseases may be associated with increased plasmatic cholesterol due to an abnormal lipoprotein – lipoprotein X (LpX). Correcting the underlying cause of cholestasis is the critical treatment of LpX-associated hypercholesterolemia without any proven benefit from conventional lipid-lowering agents. In some situations, plasma exchange may apply to prevent associated complications, such as hyperviscosity syndrome. The authors present the case of a 44-year-old man with orbital inflammatory pseudotumor on prednisolone, admitted due to hepatocellular and cholestatic lesion and severe hypercholesterolemia. Laboratory investigation established that hepatitis E virus was responsible for liver injury and showed that LpX mediated the severe hypercholesterolemia. Reduction of the immunosuppressive load contributed to virus clearance. The consequent resolution of cholestasis and cholesterol removal by plasmapheresis allowed lipid profile normalization. The authors report the first case of LpX-associated hypercholesterolemia in a patient with hepatitis E-induced cholestasis and revisit the role of the liver in lipid metabolism.
Multiple Dural Arteriovenous Fistulas as the Mystery of Rapidly Progressive Dementia with Bilateral Thalamic Lesions
Authors: Zhan Yu , Al-Nusaif Murad , Xu Chang , Li Jiahao , Zhao Li , Wang Feng , Dong Chunbo
Published: October 2022

Dementia is a gradual and irreversible loss of higher mental function, particularly memory. Dural arteriovenous fistulas (DAVFs) are one of the rare causes of a rapid decline in cognitive function, which can be curable. DAVFs are pathological shunts between the dural artery and the dural venous sinus, dural vein, or cortical vein. Here, we present a case that initially manifested nausea and dizziness and developed rapidly progressive dementia caused by DAVFs in the left transverse sinus-sigmoid sinus junction area and the sinus confluence area, combined with cerebral venous sinus thrombosis. Moreover, our case has multiple DAVFs that cause bilateral thalamic lesions and rapidly progressive dementia called thalamic dementia, which is infrequent and often misdiagnosed. His symptoms have improved after receiving endovascular embolization treatment. In addition to presenting our case, we conducted a systemic literature review to summarize how familiarity with the manifestation and early diagnosis of bilateral thalamic lesions caused by DAVFs can lead to earlier and more effective therapy.
Outcomes of Treatment of Multicanal Benign Paroxysmal Positional Vertigo by Means of TRV Chair
Authors: Baydan-Aran Mine , Binay-Bolat Kübra , Hançer-Aslan Gülçin , Avcı-Can Selen , Yeğin Seçil , Öztaş Seher , Tokgoz-Yilmaz Suna , Yorulmaz İrfan
Published: October 2022

Introduction: Benign paroxysmal positional vertigo (BPPV) is one of the most common causes of peripheral vertigo and can manifest in different forms. Multicanal BPPV is rare and difficult to treat. TRV Chair is a method that offers diagnosis and treatment at the same time. The aim was to analyze the outcomes of treatment of multicanal BPPV by means of TRV Chair and compare those results with manual maneuvers. Methods: Forty-nine subjects aged 26–73 and diagnosed with multicanal BPPV were included. Appropriate maneuvers were performed on affected canals either by TRV Chair or manually. The number of maneuvers required by TRV Chair and manual maneuver were analyzed and compared. Results: The mean number of maneuvers performed until the treatment was completed in manual treatment group was 2.57 ± 1.03, and in TRV group, it was 2.25 ± 1.16. No significant difference was observed between the number of maneuvers performed in either group until the end of the treatment (p > 0.05). Discussion/Conclusions: Neither TRV Chair nor manual maneuvers were superior to the other in the treatment of multicanal BPPV cases.
Aberrant B-Cell Activation in Systemic Lupus Erythematosus
Authors: Kang Na , Liu Xiaohang , You Xujie , Sun Wenbo , Haneef Kabeer , Sun Xiaolin , Liu Wanli
Published: October 2022

Background: B lymphocytes (B cells) are essential in humoral response, and their activation is an important first step for the production of antibodies. However, aberrant B-cell activation is common in the development and progression of autoimmune diseases including systemic lupus erythematosus (SLE), which is characterized by the generation of superfluous autoantibodies. SLE exhibits clinical manifestation such as excessive inflammation and tissue damage. This review aims to summarize the recent emerging studies on aberrant B-cell activation and the associated concurrent therapeutic targets in SLE. Summary: Aberrant B-cell activation is closely associated with the pathogenesis of SLE. Among a variety of mechanisms, dysregulations of B-cell receptor (BCR), toll-like receptor (TLR), and B-cell activating factor receptor (BAFF-R) pathways are the common and dominating factors involved in aberrant B-cell activation. These aberrant signaling transductions play diverse and integrated roles in the development and the pathogenesis of SLE. Therapies targeting aberrant B-cell activation have shown promising efficacy in achieving the clinical alleviation of SLE, suggesting the discovery of new drug targets from these aberrant signaling pathways is imminent. Here, an integrated survey or review of published high-throughput sequencing database covering RNAs of B cells from SLE versus criteria-matched healthy controls highlights that reported signaling molecules in BCR pathway (VAV2, PLC-γ2), TLR pathway (TLR9, P105, IRF7, TAB1), and BAFF-R pathway (SDF-1α) are attitudinally upregulated in SLE patients. This review thus suggests the concurrent and future therapeutic targets and potential biomarkers in both basic and clinical studies of SLE. Key Messages: This review focuses on core B-cell signaling pathways, discussing the progress in the role of aberrant B-cell activation during the pathogenesis of SLE. This review also highlights the signaling molecules from published studies and database for the possible prevention and treatment targets serving the future clinical treatments of SLE.
Eigenschaften uvealer Melanome Stadium IV mit synchroner Metastasierung – eine internationale Studie
Authors: Seibel Ira
Published: October 2022

Objective: To analyse ocular and systemic findings of patients presenting with systemic metastasis. Methods: and analysis: It is an international, multicentre, internet-enabled, registry-based retrospective data analysis. Patients were diagnosed between 2001 and 2011. Data included: primary tumour dimensions, extrascleral extension, ciliary body involvement, American Joint Committee on Cancer (AJCC)-tumour, node, metastasis staging, characteristics of metastases. Results: Of 3610 patients with uveal melanoma, 69 (1.9%; 95% CI 1.5 to 2.4) presented with clinical metastasis (stage IV). These melanomas originated in the iris, ciliary body and choroid in 4%, 16% and 80% of eyes, respectively. Using eighth edition AJCC, 8 (11%), 20 (29%), 24 (35%), and 17 (25%) belonged to AJCC T-categories T1-T4. Risk of synchronous metastases increased from 0.7% (T1) to 1.5% (T2), 2.6% (T3) and 7.9% (T4). Regional lymph node metastases (N1a) were detected in 9 (13%) patients of whom 6 (67%) had extrascleral extension. Stage of systemic metastases (known for 40 (59%) stage IV patients) revealed 14 (35%), 25 (63%) and 1 (2%) had small (M1a), medium-sized (M1b) and large-sized (M1c) metastases, respectively. Location of metastases in stage IV patients were liver (91%), lung (16%), bone (9%), brain (6%), subcutaneous tissue (4%) and others (5%). Multiple sites of metastases were noted in 24%. Compared with the 98.1% of patients who did not present with metastases, those with synchronous metastases had larger intraocular tumours, more frequent extrascleral extension, ciliary body involvement and thus a higher AJCC T-category. Conclusions: Though higher AJCC T-stage was associated with risk for metastases at diagnosis, even small T1 tumours were stage IV at initial presentation. The liver was the most common site of metastases; however, frequent multiorgan involvement supports initial whole-body staging.
High Levels of Heat Shock Protein 47 in the Aqueous Humor of Patients with Acute Primary Angle Closure
Authors: Wang Wei-wei , Yu Hai-zhen , Yang Xin , Xu Qian-qian , Yan Huan-huan , Liu Jian-rong
Published: October 2022

Introduction: Acute primary angle closure (APAC) is often characterized by acute elevation of intraocular pressure accompanied by severe ocular and systemic symptoms. Excessive collagen accumulation, which can be caused by upregulated heat shock protein 47 (HSP47) expression, can produce scarring in rat conjunctival blebs. Meanwhile, the presence of HSP47 in human aqueous humor and its levels are yet to be determined. Methods: We examined 32 consecutive patients with APAC and 16 age-matched participants without APAC scheduled for cataract surgery who were enrolled as a control group. Aqueous humor samples were collected from all subjects at the time of surgery and compared between the subjects with and without APAC. Results: The levels of HSP47 in the aqueous humor of patients with APAC (1,210.4 ± 450.2 pg/mL) were found to be significantly increased (p = 0.001) compared with those in the control group (863.4 ± 240.0 pg/mL). Notably, the levels of HSP47 negatively correlated with the age of patients with APAC (p = 0.023). Conclusion: HSP47 was upregulated in the aqueous humor of patients with APAC and may play a role in scarring after trabeculectomy for APAC.
Etiology and Antibiotic Susceptibility Patterns of Urinary Tract Infections in Children in a General Hospital in Kuwait: A 5-Year Retrospective Study
Authors: Al Benwan Khalifa , Jamal Wafaa
Published: October 2022

Objective: The aim of this study was to determine the bacterial profile and prevalence of antibiotic resistance patterns of uropathogens, as well as to evaluate the problem with extended-spectrum β-lactamases (ESBLs)-producing isolates, causing urinary tract infections (UTIs) in children in Al-Amiri Hospital, Kuwait, over a 5-year period. Materials and Methods: Significant isolates from symptomatic pediatric patients with UTIs from January 2017 to December 2021 were identified by conventional methods and by the VITEK 2 identification card system. Antimicrobial susceptibility testing was performed by the disk diffusion method for Gram-positive organisms and an automated VITEK 2 system for Gram-negative organisms. ESBL-producing Enterobacterales were detected by the double-disk diffusion method and VITEK 2 system. Results: Significant bacteriuria was detected in 13.7% of the 9,742 urine samples. Escherichia coli accounted for 67.3% of these, followed by Klebsiella pneumoniae (8.9%), Proteus spp. (5.7%), and Enterococcus spp. (7.4%), respectively. High resistance rates were observed among the Enterobacterales against ampicillin, cephalothin, nitrofurantoin, amoxicillin/clavulanic acid, and trimethoprim-sulfamethoxazole. The prevalence of ESBL-producing E. coli and K. pneumoniae was 26% and 55%, respectively. The most sensitive among the antibiotics tested for Gram-negative organisms were meropenem, amikacin, gentamicin, and piperacillin/tazobactam, while the antibiotics tested for Gram-positive organisms were vancomycin, ampicillin, linezolid, and nitrofurantoin. Conclusion: E. coli remains the most common uropathogen. A high percentage of uropathogens causing UTI in children were highly resistant to the first- and second-line antibiotics for the therapy of UTI. ESBL-producing bacteria were highly prevalent in children in our hospital. Local antibiograms should be used to assist with empirical UTI treatment.
Wireless hand motion controlled robotic arm using flex sensors
Authors: Mohd Yusoff Zakiah , Aminah Nordin Siti , Munira Markom Arni , Nadia Mohammad Nurul
Published: October 2022

In today's world, in almost all industries, much of the work is performed by robots or robotic arms with varying degrees of freedom (DOF) as necessary. The aim of this study is to adjust the perception of remote controls for manually controlled robotic-arm operation. This paper offers a way of thinking and a way to eradicate the keys, joysticks and replace them with some of the more intuitive strategy that is to operate the full robotic arm by hand movements operators. The robotic arm is constructed in such a way that it consists of two movable fingers and other movement, which is, a spreading elbow and the up down movement. The robotic arm is designed to mimic the motions of human hands using a hand glove. The hand glove consists of 3 flex sensors for controlling the motions of the finger, the elbow, and other movements. Servo motors are the actuators used by the robotic arm. The proposed electronics device recognizes a basic hand gesture that will be made in real lifetime and will relay valued signals wirelessly through the RF module.
D flip-flop design by adiabatic technique for low power applications
Authors: Sanadhya Minakshi , Kumar Sharma Devendra
Published: October 2022

Indigital circuits, energy reduction is the most important parameter in the design of handy and battery-operated devices. Flip- flop is an important component in any digital system. By improving the performance of flip-flop, complete system performance is better. This paper addresses the design of D flip-flop using direct current diode-based positive feedback adiabatic logic (DC-DB PFAL) at various frequencies at 45nm technology node. Further, the layout for the proposed design is also presented. The performance analysis is carried out for delay, power dissipation, power delay product and transistor count. Circuit simulation is done by using cadence virtuoso tool at 10 MHz and 100 MHz frequencies. The results show an improvement in power dissipation of 18% with less transistors count compared to exiting designs in the literature.
Analysis the efficiency of multi-input-multi-output (MIMO) transmit receive systems
Authors: Hlal Mutlaq Ali , Ayad Saad Mohammed , Hassan Tata Faris , Thiab Hasan Ghanim
Published: October 2022

Transmit antennas are chosen in multi-input-multi-output (MIMO) systems. Effective in improving system capacity while lowering RF connection costs and simplifying the system. Complete method with greatest accuracy for joint transmits and receive antenna selection (JTRAS), capable of scanning all subsets of both transmitting and receiving antennas for the optimal solution. However, when as the number of antennas but also computational complexity increase grows too great, limiting its application. Antennas are coded fractionally channel capacity maximizing coding is used as a basic criterion in this paper, and an intelligent algorithm Particle swarm algorithm, generic algorithm are used to pick antennas. The simulation results show that both algorithms are capable of performing antenna selection.
A miniaturized dual-band planar antenna with a square ring defected ground structure for 5G millimetre-wave applications
Authors: Mamarou Diallo Mamadou , Bernard Onyango Konditi Dominic , Videme Bossou Olivier
Published: October 2022

The high demand for wireless wideband services has led to evolving of a new mobile network standard, which is known as ‘5G’. For 5G to meet the essentials in terms of bandwidth, the industry should leverage the mm-Wave band (24-300 GHz). Further, miniaturized antennas that operate in multiple frequency bands are required for future space-constrained devices. In this manuscript, a compact dual-band circular microstrip antenna which has a square ring defected ground structure (SR-DGS) is investigated for 5G mobile systems. The design is accomplished using Ansys-HFSS 2021R1. The Rogers RT/duroid (5880) substrate, which has a permittivity of 2.2, a tangent loss of 0.0009, and a thickness of 1.575 mm, is used as a dielectric material. The antenna has physical dimensions of 5x4x1.575 mm3 with an electrical size of 0.458λ0 x0.366λ0 x0.144λ0 ; λ0 represents the wavelength in free space at 27.50 GHz. Impedance bandwidths of 1.34 GHz (27.50 GHz-28.84 GHz) and 2.26 GHz (37.74 GHz-40 GHz) are achieved at the 28 GHz and 38 GHz bands, respectively. The antenna resonates at 28.1875 GHz and 38.5625 GHz with respective gains of 7.2 dB and 7.65 dB. The proposed antenna is a promising candidate for 5G communications due to its miniaturized size.
Algebraic fields and rings as a digital signal processing tool
Authors: Matrassulova Dinara Kutlimuratovna , Vitulyova Yelizaveta Sergeevna , Konshin Sergey Vladimirovich , Suleimenov Ibragim Esenovich
Published: October 2022

It is shown that algebraic fields and rings can become a very promising tool for digital signal processing. This is mainly due to the fact that any digital signals change in a finite range of amplitudes and, therefore, there are only a finite set of levels that can correspond to the amplitudes of a signal reduced to a discrete form. This allows you to establish a one-to-one correspondence between the set of levels and such algebraic structures as fields, rings, etc. This means that a function that takes values in any of the algebraic structures containing a finite set of elements can serve as a model of a signal reduced to a discrete form. A special case of such a signal model are functions that take values in Galois fields. It is shown that, along with Galois fields, in certain cases, algebraic rings contain zero divisors can be used to construct signal models. This representation is convenient because in this case it becomes possible to independently operate with the digits of the number that enumerates the signal levels. A simple and intuitive method for constructing rings is proposed, based on an analogy with the method of algebraic extensions.
Priority-based market clearing model for off-grid P2P energy trading
Authors: Wan Zainal Abidin Wan Azlan , Ling Sieng Yew Alan
Published: October 2022

Standalone solar PV power system is being used as an option for electrification in remote areas around the world providing basic electricity needs. However, the approach suffers from power mismatch and energy efficiency issues. This paper proposes market-clearing model peer-to-peer energy trading (P2PET) based on multiple standalone solar power system design specification in rural Sarawak, Malaysia. The proposed system combined multiple standalone solar PV system within the community through P2PET trading concept. P2PET creates the platform for energy transaction between each system and even support business such as a workshop to operate high-power electrical appliances. As energy generation is constrained in an off-grid system, the proposed market-clearing model prioritizes the energy trading between the seller and business buyer who bring more benefit to the community. Subsequently, participants in energy trading have a selection of strategies to maximize personal benefits such as profit earning or energy sufficiency. Simulation studies are applied to verify the performance of the proposed model which increases energy efficiency, improves the local economy, and maximizes the community’s welfare from electrification.
Improvement of conjugate gradient methods for removing impulse noise images
Authors: A. Hassan Basim , Ahmed A. Abdullah Ali
Published: October 2022

Optimization problems occur in most disciplines like engineering, physics, mathematics, economics, administration, commerce, social sciences, and even politics. The conjugate coefficient is the cornerstone of conjugate gradient algorithms with the desired conjugate property. In this study, we discovered fresh second order information for the Hessian from the target function, which might lead to a new search direction. Based on a unique search direction, we proposed the update formula and nonlinear conjugate gradient technique. Under Wolfe line search and moderate objective function assumptions, the strategy has acceptable descent property and is always globally convergent. According to numerical results, the technique is successful and competitive in recovering the original picture from an image corrupted by impulsive noise.
Variational selective segmentation model for intensity inhomogeneous image
Authors: Christy Saibin Tammie , Kadir Jumaat Abdul
Published: October 2022

Variational selective image segmentation models aim to extract a particular object in an image depending on a set of user-defined prior points. The current model suffers from high computational costs due to the traditional total variation function that results in a slow segmenting process. In addition, it is not designed to segment images with intensity inhomogeneities. In this research, we formulate a new variational selective image segmentation model based on the Gaussian function. A Gaussian function is proposed to replace the traditional total variation function to regularize the variational level set function. To segment images with intensity inhomogeneities, the local image fitting idea was in corporate into the formulation. The efficiency of the proposed model was then assessed by recording the computation time while the accuracy was measured using Jaccard and Dice similarity values. Numerical experiments using synthetic, natural, and medical images demonstrate that the proposed model is about 6 times faster than the existing model, while the Jaccard and Dice values are about 11% and 7% higher, respectively, compared to the existing model. In the future, this research can be extended further into a 3-dimensional modeling and vector-valued image framework.
Hyperspectral image classification using Hyb-3D convolution neural network spectral partitioning
Authors: Ravi Kondal Easala , Sankar Barpanda Soubhagya
Published: October 2022

Hyperspectral image classification (HSIC) on remote sensing imaging has brought immersive achievement using artificial intelligence technology. In deep learning convolution neural networks (CNN), 2D-CNN, and 3D-CNN methods are widely used to classify the spectral-spatial bands of hyperspectral images (HSI). The proposed Hybrid 3D-CNN (H3D-CNN) model framework for deeper features extraction predicts classification accuracy in supervised learning. The model reduces the narrow gap between supervised and unsupervised learning and the complexity and cost of the previous models. The HSI classification analysis is carried out on real-world data sets of Indian pines Salinas datasets captured by Airborne visible, infrared imaging spectrometer (AVIRIS) sensors that performed superior classification accuracy results.
Osteoporosis detection using convolutional neural network based on dual-energy X-ray absorptiometry images
Authors: Z. Mohammed Abulkareem , E. George Loay
Published: October 2022

Osteoporosis is one of the most common diseases that affect the bones of adults, especially women in menopause, and the reason for this is due to the lack of bone mineral density bone mineral density (BMD). BMD can be measured by X-ray and dual energy X-ray absorptiometry (DEXA) images, this article, focused on using DEXA images for Osteoporosis detection. At first, the original image must passed through the preprocessing stage, during which the noisy parts is reduced, and the useless parts are eliminated, and then the contrast between adjacent areas is increased and the area of interest is allocated. After that, the image is passed in a deep learning model in order to extract the unique features on the basis of which each image is classified. The classification result was excellent with 98% accuracy. The used dataset is “Osteoporosis DEXA scans images” of Spine from Pakistan.
Earthquake prediction in Iraq using machine learning techniques
Authors: Badr Jarah Nada , Mahdi Hashim Kadhim , Hanon Hassin Abbas
Published: October 2022

This study deals with addressing the scientific achievements and the history of earthquake prediction in Iraq, in addition to attempting to discuss the possibility of machine learning to predict earthquakes from a theoretical perspective. The idea of predicting earthquakes gives at least a little time to protect people and reduce earthquake damage. In Iraq, we notice an increase in the occurrence of earthquakes, especially in the southern regions, where they form a strange phenomenon because they are plain areas and far from the seismic fault line, due to the errors that accompany excessive oil extraction and in random and unstudied ways, and geological studies raise fears in predicting an increase in earthquakes for the coming years. We have explored the possibility of applying machine learning technology to predict earthquakes in Iraq, and follow-up recording of tremors at different stations in Iraq through three centers of seismic sensor networks. In addition to the earthquake catalog in Iraq (1900-2019). This study may pave the way for more research to develop an integrated and accurate earthquake prediction system using machine-learning technologies.
Hide text in an image using Blowfish algorithm and development of least significant bit technique
Authors: Kareem Salim Sarah , Majid Msallam Mohammed , Ismail Olewi Huda
Published: October 2022

Recently, there is increasing interest in data transfer between many different devices. Data must be encrypted before being sent so that the intended receiver can only read it to prevent unauthorized access and processing of a secret message. This paper suggests two levels of security to secure import messages that are sent via the internet. A secret text is encrypted by a Blowfish algorithm, then the secret text is hidden in an image by the least significant bit (LSB) technique. The LSB method of concealing important information was developed to conceal at least the first of at least two bits, depending on the cover data. The resolution of an image is raised in our work where the sender selects a stego image with a high peak signal to noise ratio (PSNR). The receiver knows all the necessary information for decoding by the first nine pixels. The PSNR is used for evaluating the resolution of the image to check robustness at sending. The number of PSNR illustrates in our proposal that the resolution of the image is near the original LSB technique, but the embedding is more randomly robust in steganography.
Hybrid approach for multi-objective optimization path planning with moving target
Authors: M. Abed Baraa , M. Jasim Wesam
Published: October 2022

Path planning algorithms are the most significant area in the robotics field. Path planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Path planning optimization refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. Several objectives are considered as part of this study, including path security, length, and smoothness, when planning paths for autonomous mobile robots in a dynamic environment with a moving target. Particle swarm optimization (PSO) algorithms are combined with bat algorithms (BA) to make a balance between exploration and exploitation. PSO algorithms used to optimize two important parameters of the bat algorithm. The proposed solution is tested through several simulations based on varying scenarios. The results demonstrate that mobile robots can travel clearly and safely along short paths and smoothly, proving this method's efficiency.
Convolutional neural network-based crop disease detection model using transfer learning approach
Authors: Adebayo Segun , Oluwatobi Aworinde Halleluyah , Akinwunmi Akinwale O. , Ayandiji Adebamiji , Olalekan Monsir Awoniran
Published: October 2022

Crop diseases disrupt the crop's physiological constitution by affecting the crop's natural state. The physical recognition of the symptoms of the various diseases has largely been used to diagnose cassava infections. Every disease has a distinct set of symptoms that can be used to identify it. Early detection through physical identification, however, is quite difficult for a vast crop field. The use of electronic tools for illness identification then becomes necessary to promote early disease detection and control. Convolutional neural networks (CNN) were investigated in this study for the electronic identification and categorization of photographs of cassava leaves. For feature extraction and classification, the study used databases of cassava images and a deep convolutional neural network model. The methodology of this study retrained the models' current weights for visual geometry group (VGG-16), VGG-19, SqueezeNet, and MobileNet. Accuracy, loss, model complexity, and training time were all taken into consideration when evaluating how well the final layer of CNN models performed when trained on the new cassava image datasets.
Application of hybrid TLBO-PSO algorithm for allocation of distributed generation and STATCOM
Authors: Ansari Anzum , C. Byalihal Shankarlingappa
Published: October 2022

This paper proposes a hybrid teaching learning-based optimization- particle swarm optimization (TLBO-PSO) Algorithm for optimal distributed generation (DG) and STATCOM placement. A multi-objective formulation is developed that optimizes the DG and STATCOM placement in IEEE 33 and real-time 52 bus distribution system. The objective function formulated involves maximizing cost-benefit and voltage stability factors while minimizing network security index and power losses. Simulation is carried out in MATLAB/Simulink for different systems and different cases. The results for single and multiple STATCOM and DG are compared with TLBO and PSO algorithm and the results exhibit better convergence performance with the hybrid optimization algorithm.
Support system of self-assessment and gap analysis for new normal tourism standards
Authors: Prachayagringkai Soawanee , Buranarach Marut , Wuttidittachotti Pongpisit
Published: October 2022

Tourism after the outbreak of the emerging epidemic of COVID-19 has drastically changed. Tourist attractions will be certified with Green National Park and New Normal Standards. Starting in the year 2021 onwards, Thailand's national parks are important tourist destinations, of which 155 nationwide will be subject to complying with such standards to ensure safety, hygiene and environmentally friendly service starting in the year 2021 onwards. This research aims to develop a support system for self-assessment and gap analysis based on Smart Self-Assessment for New Normal Tourism Standards to enable the national parks to assess themselves and be prepared for future actual assessments. The system development focuses on user data import design and report output, system performance test, self-assessment score percentage difference tests, and system performance evaluation by the experts. The percentage difference of self-assessment scores is found at 0.0 for all items after adding details in some of the work lists based on the experts’ opinions, whereas, the performance testing indicates that the system developed is applicable and highly efficient (= 4.40, S.D.= 0.54).
Digital forensics on Tencent QQ-instant messaging service in China
Authors: Li Yunkun , Mogos Gabriela
Published: October 2022

Data packet analysis targeting instant messaging (IM) applications has become one of the most mentioned case examples in the digital forensic industry, considering that the forensic engineers can extract valuable information by analysing the data packets used by the IM software. The crucial part of this process is to accomplish a series of research and investigation, in addition to correctly implement the related forensics tools. This paper is intended to use QQ, a popular IM software in China, as an experiment example, in cooperation with various tools from Kali Linux, a digital forensics-oriented Linux distribution, to present the complete process of the data packet analysis operation. The result concludes from the experiment may be able to provide constructive suggestions to other related digital forensics cases.
An adaptive algorithm based on principal component analysis-deep learning for anomalous events detection
Authors: K. Abbas Zainab , A. Al-Ani Ayad
Published: October 2022

One of the most often used applications of human activity detection is anomaly detection, which is covered in this paper. Providing security for a person is a key issue in every community nowadays because of the constantly expanding activities that pose danger, from planned violence to harm caused by an accident. Existing classical closed-circuit television considered is insufficient since it needs a person to stay awake and constantly monitor the cameras, which is expensive. In addition, a person's attention decreases after a certain time. For these reasons, the development of an automated security system that can identify suspicious activities in real-time and quickly aid victims is required. Because identifying activity must be with high accuracy, and in the shortest possible time. We adopt an adaptive algorithm based on the combination of machine learning (ML), principal component analysis (PCA) and deep learning (DL). The UCF-crime dataset was used for the experimentation in this work. Where the area under the curve (AUC) with the proposed approach was equal to 94.21% while the detection accuracy was equal to 88.46% on the test set database. The suggested system has demonstrated its robustness and accomplishment of the best accuracy when compared with earlier designed systems.
Solar panels overheating protection: a review
Authors: Laabab Imad , Ziani Said , Benami Abdellah
Published: October 2022

Reduced energy consumption is a direct result of building optimization. Solar panels and heat detectors can provide the necessary power. After years of refinement and development, methods for regulating solar panels' output are finally finding their way into everyday life, where they are having a dramatic impact on the foundations of sustainability. This document provides an up-to-date assessment of several strategies for preventing solar panels from overheating, all of which serve to boost their efficiency and prolong their service life. It begins with a brief background on protection technologies before moving on to a survey of recently filed patent applications that address this topic. The study concludes with a proposed assessment of the strengths and shortcomings of these safeguards. We anticipate that the information included in this review paper will be an invaluable tool for scholars and researchers.