bluuebunny/crossref_metadata_2025 huggingface dataset:
Small Cell Lung Cancer Plasticity Enables NFIB-Independent Metastasis
Authors: Ko Julie H. , Lambert Kyle E. , Bhattacharya Debadrita , Lee Myung Chang , Colón Caterina I. , Hauser Haley , Sage Julien
Published: November 2023

Metastasis is a major cause of morbidity and mortality in patients with cancer, highlighting the need to identify improved treatment and prevention strategies. Previous observations in preclinical models and tumors from patients with small cell lung cancer (SCLC), a fatal form of lung cancer with high metastatic potential, identified the transcription factor NFIB as a driver of tumor growth and metastasis. However, investigation into the requirement for NFIB activity for tumor growth and metastasis in relevant in vivo models is needed to establish NFIB as a therapeutic target. Here, using conditional gene knockout strategies in genetically engineered mouse models of SCLC, we found that upregulation of NFIB contributes to tumor progression, but NFIB is not required for metastasis. Molecular studies in NFIB wild-type and knockout tumors identified the pioneer transcription factors FOXA1/2 as candidate drivers of metastatic progression. Thus, while NFIB upregulation is a frequent event in SCLC during tumor progression, SCLC tumors can employ NFIB-independent mechanisms for metastasis, further highlighting the plasticity of these tumors.

Significance:

Small cell lung cancer cells overcome deficiency of the prometastatic oncogene NFIB to gain metastatic potential through various molecular mechanisms, which may represent targets to block progression of this fatal cancer type.
Supplementary Table S4 from Assessing the Genomic Landscape of Cervical Cancers: Clinical Opportunities and Therapeutic Targets
Authors: Friedman Claire F. , Ravichandran Vignesh , Miller Kathryn , Vanderbilt Chad , Zhou Qin , Iasonos Alexia , Vivek Malavika , Mishra Pamela , Leitao Mario M. , Broach Vance , Sonoda Yukio , Kyi Chrisann , Zamarin Dmitriy , O'Cearbhaill Roisin E. , Konner Jason , Berger Michael F. , Weigelt Britta , Momeni Boroujeni Amir , Park Kay J. , Aghajanian Carol , Solit David B. , Donoghue Mark T.A.
Published: November 2023

Clinical trial enrollment and clinical outcomes
Supplementary Figure S3 from Assessing the Genomic Landscape of Cervical Cancers: Clinical Opportunities and Therapeutic Targets
Authors: Friedman Claire F. , Ravichandran Vignesh , Miller Kathryn , Vanderbilt Chad , Zhou Qin , Iasonos Alexia , Vivek Malavika , Mishra Pamela , Leitao Mario M. , Broach Vance , Sonoda Yukio , Kyi Chrisann , Zamarin Dmitriy , O'Cearbhaill Roisin E. , Konner Jason , Berger Michael F. , Weigelt Britta , Momeni Boroujeni Amir , Park Kay J. , Aghajanian Carol , Solit David B. , Donoghue Mark T.A.
Published: November 2023

Supp Figure 3: Lollipop mutation diagram of hotspot alterations in ERBB2, PIK3CA, and KRAS in the Pan-Cancer Cohort
Supplementary Table S2 from Effective Reprogramming of Patient-Derived M2-Polarized Glioblastoma-Associated Microglia/Macrophages by Treatment with GW2580
Authors: Fermi Valentina , Warta Rolf , Wöllner Amélie , Lotsch Catharina , Jassowicz Lena , Rapp Carmen , Knoll Maximilian , Jungwirth Gerhard , Jungk Christine , Dao Trong Philip , von Deimling Andreas , Abdollahi Amir , Unterberg Andreas , Herold-Mende Christel
Published: November 2023

Antibodies for flow cytometry
Supplementary Material and Methods S1 from Effective Reprogramming of Patient-Derived M2-Polarized Glioblastoma-Associated Microglia/Macrophages by Treatment with GW2580
Authors: Fermi Valentina , Warta Rolf , Wöllner Amélie , Lotsch Catharina , Jassowicz Lena , Rapp Carmen , Knoll Maximilian , Jungwirth Gerhard , Jungk Christine , Dao Trong Philip , von Deimling Andreas , Abdollahi Amir , Unterberg Andreas , Herold-Mende Christel
Published: November 2023

Supplementary Material and Methods
Supplementary Figure S6 from Effective Reprogramming of Patient-Derived M2-Polarized Glioblastoma-Associated Microglia/Macrophages by Treatment with GW2580
Authors: Fermi Valentina , Warta Rolf , Wöllner Amélie , Lotsch Catharina , Jassowicz Lena , Rapp Carmen , Knoll Maximilian , Jungwirth Gerhard , Jungk Christine , Dao Trong Philip , von Deimling Andreas , Abdollahi Amir , Unterberg Andreas , Herold-Mende Christel
Published: November 2023

Changes in the cellular composition and proliferative activity in pGBM patient-derived tumor organoids upon treatment with PLX3397 or BLZ945.
Supplemental Tables 1 from Clinical and Genomic Landscape of FGFR3-Altered Urothelial Carcinoma and Treatment Outcomes with Erdafitinib: A Real-World Experience
Authors: Guercio Brendan J. , Sarfaty Michal , Teo Min Yuen , Ratna Neha , Duzgol Cihan , Funt Samuel A. , Lee Chung-Han , Aggen David H. , Regazzi Ashley M. , Chen Ziyu , Lattanzi Michael , Al-Ahmadie Hikmat A. , Brannon A. Rose , Shah Ronak , Chu Carissa , Lenis Andrew T. , Pietzak Eugene , Bochner Bernard H. , Berger Michael F. , Solit David B. , Rosenberg Jonathan E. , Bajorin Dean F. , Iyer Gopa
Published: November 2023

Supplemental Tables 1 and 2
Supplemental Figures 1 from Clinical and Genomic Landscape of FGFR3-Altered Urothelial Carcinoma and Treatment Outcomes with Erdafitinib: A Real-World Experience
Authors: Guercio Brendan J. , Sarfaty Michal , Teo Min Yuen , Ratna Neha , Duzgol Cihan , Funt Samuel A. , Lee Chung-Han , Aggen David H. , Regazzi Ashley M. , Chen Ziyu , Lattanzi Michael , Al-Ahmadie Hikmat A. , Brannon A. Rose , Shah Ronak , Chu Carissa , Lenis Andrew T. , Pietzak Eugene , Bochner Bernard H. , Berger Michael F. , Solit David B. , Rosenberg Jonathan E. , Bajorin Dean F. , Iyer Gopa
Published: November 2023

Supplemental Figures
Supplementary Table 2 from Integration of Genomic Sequencing Drives Therapeutic Targeting of PDGFRA in T-Cell Acute Lymphoblastic Leukemia/Lymphoblastic Lymphoma
Authors: Paolino Jonathan , Dimitrov Boris , Apsel Winger Beth , Sandoval-Perez Angelica , Rangarajan Amith Vikram , Ocasio-Martinez Nicole , Tsai Harrison K. , Li Yuting , Robichaud Amanda L. , Khalid Delan , Hatton Charlie , Gillani Riaz , Polonen Petri , Dilig Anthony , Gotti Giacomo , Kavanagh Julia , Adhav Asmani A. , Gow Sean , Tsai Jonathan , Li Yen Der , Ebert Benjamin L. , Van Allen Eliezer M. , Bledsoe Jacob , Kim Annette S. , Tasian Sarah K. , Cooper Stacy L. , Cooper Todd M. , Hijiya Nobuko , Sulis Maria Luisa , Shukla Neerav N. , Magee Jeffrey A. , Mullighan Charles G. , Burke Michael J. , Luskin Marlise R. , Mar Brenton G. , Jacobson Matthew P. , Harris Marian H. , Stegmaier Kimberly , Place Andrew E. , Pikman Yana
Published: November 2023

Primers used for site directed mutagenesis. The table lists the forward and reverse primers used in the creation of PDGFRA p.D842, p.D845, and p.R383 mutations.
Supplementary Figure S4 from Survivin Dendritic Cell Vaccine Safely Induces Immune Responses and Is Associated with Durable Disease Control after Autologous Transplant in Patients with Myeloma
Authors: Freeman Ciara L. , Atkins Reginald , Varadarajan Indumathy , Menges Meghan , Edelman Jeffrey , Baz Rachid , Brayer Jason , Castaneda Puglianini Omar , Ochoa-Bayona Jose Leonel , Nishihori Taiga , Shain Kenneth H. , Shah Bijal , Chen Dung Tsa , Kelley Linda , Coppola Domenico , Alsina Melissa , Antonia Scott , Anasetti Claudio , Locke Frederick L.
Published: November 2023

Supplemental figure S4: Humoral Immunity at baseline and post vaccination against survivin and other common tumor associated antigens (TAA)
Data from RANK Expression as an Independent Predictor for Response to Neoadjuvant Chemotherapy in Luminal-Like Breast Cancer: A Translational Insight from the GeparX Trial
Authors: Link Theresa , Blohmer Jens-Uwe , Schmitt Wolfgang D. , Kuhlmann Jan Dominik , Just Marianne , Untch Michael , Stotzer Oliver , Fasching Peter A. , Thill Marc , Reinisch Mattea , Schneeweiss Andreas , Wimberger Pauline , Seiler Sabine , Huober Jens , Jackisch Christian , Rhiem Kerstin , Hanusch Claus , Sinn Bruno V. , Nekljudova Valentina , Loibl Sibylle , Denkert Carsten
Published: November 2023

AbstractPurpose:
The GeparX study investigated whether denosumab as add-on treatment to nab-paclitaxel–based neoadjuvant chemotherapy (NACT) with two different schedules (125 mg/m² weekly vs. day 1, 8 every 22 days) may increase pathologic complete response (pCR) rate. The addition of denosumab to NACT did not improve pCR rates as recently published. In this study, we investigated whether receptor activator of nuclear factor-kappa B (RANK) expression, as part of the denosumab target pathway: (i) may retrospectively identify a subgroup of patients with additional clinical benefit of denosumab or (ii) may predict response to nab-paclitaxel NACT.
Experimental Design:
RANK protein was IHC-stained on pre-therapeutic core biopsies from patients of the GeparX study (n = 667) with the antibody RANK/Envision System HRP (DAB) and was analyzed for the percentage of membranous RANK tumor cell staining (>5% RANKhigh vs. ≤5% RANKlow).
Results:
We could not identify any patient subgroup with differential response under denosumab add-on treatment in patients with RANKhigh expression [139/667, 20.8%; OR, 0.86; 95% confidence interval (CI), 0.44–1.68; P = 0.667] or RANKlow expression (528/667 (79.2%) OR, 1.10; 95% CI, 0.78–1.56; P = 0.589; Pinteraction = 0.528). However, the pCR rate was higher in the RANKhigh subgroup compared with RANKlow (50% vs. 39%; OR, 1.52; 95% CI, 1.04–2.21; P = 0.037). RANK expression constituted an independent predictor of response to NACT frequently in patients with luminal-like subtype (HR+/HER2−; OR, 2.98; 95% CI, 1.30–6.79; P = 0.010). No predictive value of RANK expression among the different nab-paclitaxel regimens was observed.
Conclusion:
We report RANK expression to be an independent predictive biomarker for response to NACT in patients with luminal-like breast cancer.
Data from The Landscape of Alterations from 1407 Ultra-Rare Sarcomas from the AACR GENIE Database: Clinical Implications
Authors: Denu Ryan A. , Moyers Justin T. , Gouda Mohamed A. , Conley Anthony P. , Lazar Alexander J. , Subbiah Vivek
Published: November 2023

AbstractPurpose:
Ultra-rare sarcomas (URS) comprise a group of orphan diseases with an incidence of ≤1/1,000,000 people per year. We aimed to assess clinically actionable genomic alterations in URS.
Experimental Design:
Data were extracted from the GENIE database using cBioPortal. OncoKB was used to assess for clinical actionability of mutations. Tumor mutational burden (TMB) was inferred from clinical sequencing data.
Results:
Soft tissue (ST) URS made up 23.5% of ST sarcoma cases, and bone URS made up 16.5% of bone sarcoma cases. The most commonly mutated gene in all four groups was TP53. The most common fusions involved EWSR1. The most common copy-number variations included deletions of CDKN2A and CDKN2B and amplifications of MDM2 and CDK4. TMB was generally low across all four categories of sarcoma, though there was considerable heterogeneity, with 3.8% of ST URS and 0.55% of bone URS having high TMB. We find Level 1 alterations (FDA-recognized biomarker predictive of response to an FDA-approved drug) in 10.0% of ST URS compared with 7.1% of ST non-URS, 1.1% of bone URS, and 4.5% of bone non-URS. Level 1–3 alterations (also include alterations for which there are standard-of-care drugs or clinical evidence supporting a drug) were seen in 27.8% of ST URS, 25.2% of ST non-URS, 20.9% of bone URS, and 17.4% of bone non-URS.
Conclusions:
Clinically actionable genomic alterations are seen in a substantial fraction of URS. Clinical sequencing in advanced URS has the potential to guide the treatment of a significant portion of patients with URS.
Dopamine and Alcohol: A Review of in vivo PET and SPECT Studies
Authors: Spitta Gianna , Garbusow Maria , Buchert Ralph , Heinz Andreas
Published: November 2023

Background: Alcohol-associated alterations of the dopaminergic (DA) system have been investigated via functional single-photon emission tomography (SPECT) positron emission tomography (PET) and imaging methods over many years, investigating presynaptic or postsynaptic markers, such as DA receptor and DA transporter availability, both with and without challenge. This review summarizes SPECT and PET studies on different levels of alcohol consumption to support the dimensional view of alcohol use disorder (AUD), ranging from acute consumption in social drinkers, individuals at high risk to patients with severe AUD and their association with blunted DA neurotransmission. Additionally, confounding factors of PET and SPECT studies of the DA system were discussed. Summary: The included studies provided strong evidence that acute alcohol administration in social drinkers is followed by a DA release, particularly in the ventral striatum. In participants with AUD, DA release appears to be impaired as administration of a psychostimulant is followed by a blunted striatal DA. Furthermore, in recently detoxified participants with AUD, in vivo dopamine D2 and D3 receptor availability appears to be reduced, which may be a predisposing factor or the result of a neuroadaptive process influencing drug-induced DA release. DA transporter availability is reduced in AUD, whereas findings with respect to DA synthesis capacity are controversial. Key Messages: The DA system seems to be differently impaired during the development and persistence of AUD. In total, challenge studies (acute alcohol or psychostimulant administration) seem to be more consistent in their findings and might be less prone to the effects of confounders. Long-term studies with larger samples are required to better evaluate the alterations during chronic consumption and prolonged abstinence.
Efficacy of Rituximab for Minimal Change Disease and Focal Segmental Glomerulosclerosis with Frequently Relapsing or Steroid-Dependent Nephrotic Syndrome in Adults: A Chinese Multicenter Retrospective Study
Authors: Lan Lan , Lin Yuxin , Yu Binfeng , Wang Yin , Pan Hong , Wang Huijing , Lou Xiaowei , Lang Xiabing , Zhang Qiankun , Jin Lie , Yang Yi , Xiao Liang , Chen Jianghua , Han Fei
Published: November 2023

Introduction: Rituximab has been proven effective and safe in pediatric patients with frequently relapsing or steroid-dependent nephrotic syndrome (FR/SDNS). We aimed to analyze the efficacy and safety of rituximab in adult FR/SDNS patients with minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS). Methods: A retrospective cohort study at three nephrology centers in China included adult FR/SDNS patients with biopsy-proven MCD or FSGS. Primary outcomes were relapse frequency and first relapse-free survival time. Adverse events were well recorded, and logistic regression analyses were used to investigate the risk factors of relapse. Results: Eighty-one patients (age, 25.0 years; interquartile range, 20.0–40.5; 67% males; 82.7% MCD) received an average rituximab dose of 1,393.8 ± 618.7 mg/2 years during the 2-year follow-up period. The relapse frequency, calculated as the ratio of relapse times to follow-up years, significantly decreased after rituximab treatment (0.04 [0.00, 0.08] vs. 1.71 [1.00, 2.45], p < 0.001). The first relapse-free survival time was 16.7 ± 8.0 months. Fifty-seven patients (70.4%) achieved cessation of corticosteroids and immunosuppressants within 3 months after the first rituximab infusion. Adverse events were mostly mild, and no severe treatment-related adverse events were observed. Low serum albumin level before rituximab and high CD56+CD16+ natural killer cell count after rituximab were independent risk factors of relapse within 2 years after rituximab treatment. Conclusion: Rituximab was proven an effective and safe treatment option for adult FR/SDNS patients with MCD or FSGS in maintaining disease remission and minimizing corticosteroid exposure.
Pedestrian level of service for sidewalks in Tangier City
Authors: Benhadou Marwane , El Gonnouni Amina , Lyhyaoui Abdelouahid
Published: November 2023

The pedestrian level of service (PLOS) is a measure that quantifies walkway comfort levels. PLOS defined into six categories (A, B, C, D, E, and F) each level defines the range of values, for example, a good level (best traffic condition) is defined with the letter A until reaching the worst level, F (high congestion). This article aims to define the PLOS on sidewalks considering walking conditions in Tangier City (Morocco). Sidewalks are analyzed using video recording in the urban center of Tangier City. The collected data are pedestrian flow and effective sidewalk width. Each level contains a range of values that corresponds to the pedestrian flow that defines the level of service. Clustering techniques are used to identify the threshold of each level using a self-organizing map (SOM). The results are different from those obtained with other methods because pedestrian traffic differs from country to country.
Hybrid fuel cell-supercapacitor system: modeling and energy management using Proteus
Authors: Haidoury Mohamed , Rachidi Mohammed , El Hadraoui Hicham , Laayatii Oussama , Kourab Zakaria , Tayane Souad , Ennaji Mohamed
Published: November 2023

The increasing adoption of electric vehicles (EVs) presents a promising solution for achieving sustainable transportation and reducing carbon emissions. To keep pace with technological advancements in the vehicular industry, this paper proposes the development of a hybrid energy storage system (HESS) and an energy management strategy (EMS) for EVs, implemented using Proteus Spice Ver 8. The HESS consists of a proton exchange membrane fuel cell (PEMFC) as the primary source and a supercapacitor (SC) as the secondary source. The EMS, integrated into an electronic board based on the STM32, utilizes a low-pass filter algorithm to distribute energy between the sources. The accuracy of the proposed PEMFC and SC models is validated by comparing Proteus simulation results with experimental tests conducted on the Bahia didactic bench and Maxwell SC bench, respectively. To optimize energy efficiency, simulations of the HESS system involve adjusting the hybridization rate through changes in the cutoff frequency. The analysis compares the state-of-charge (SOC) of the SC and the voltage efficiency of the fuel cell (FC), across different frequencies to optimize overall system performance. The results highlight that the chosen strategy satisfies the energy demand while preserving the FC’s dynamic performance and optimizing its utilization to the maximum.
Data quality processing for photovoltaic system measurements
Authors: Galarza Jose , Condezo Hurtado David , Saenz Bartolome
Published: November 2023

The operation and maintenance activities in photovoltaic systems use meteorological and electrical measurements that must be reliable to check system performance. The International Electrotechnical Commission (IEC) standards have established general criteria to filter erroneous information; however, there is no standardized process for the evaluation of measurements. In the present work we developed 3 procedures to detect and correct measurements of a photovoltaic system based on the single diode model. The performance evaluation of each criterion was tested with 6 groups of experimental measurements from a 3 kWp installation. Based on the error of the 3 procedures performed, the most unfavorable case has been prioritized. Then, the reduction of errors between the estimated and measured value has been achieved, reducing the number of measurements to be corrected. For the clear sky categories, the coefficient of determination is 0.9975 and 0.9961 for the high irradiance profile. Although an increase of 2.5% for coefficient of determination has been achieved, the overcast sky categories should be analyzed in more detail. Finally, the different causes of measurement error should be analyzed, associated with calibration errors and sensor quality.
Compact 3D monolithic microwave integrated circuit bandpass filter based on meander resonator for 5G millimeter-wave
Authors: Sinulingga Emerson Pascawira , Nasution Abdul Risyal
Published: November 2023

Bandpass filters for millimeter-wave band applications are typically designed using resonators. However, the design of a multilayer coplanar waveguide (CPW) monolithic microwave integrated circuit (MMIC) bandpass filter for 5G millimeter-wave band, n257 with operating frequencies from 26.5 to 29.5 GHz is still not available. Therefore, in this work, a compact bandpass filter for 5G millimeter-wave application was designed with multilayer CPW MMIC bandpass filter based on a meander resonator. The meander resonator of the bandpass filter was designed using low-loss multilayer CPW lines. In designing the bandpass filter, the resonator length and perturbation was utilized to optimize the resonance and bandwidth, and meander resonator was used to miniaturize the bandpass filter. As result, a compact bandpass filter with size of 0.75×0.75 mm2 for 5G millimeter-wave band n257 was achieved. It has bandwidth of 3 GHz, an insertion loss of -2.87 dB and a return loss of -11.1 dB at frequency 28 GHz.
An efficient adaptive reconfigurable routing protocol for optimized data packet distribution in network on chips
Authors: Goravi Sukumar Pavithra , Krishnaiah Modugu , Velluri Rekha , Satish Pooja , Nagaraju Sharmila , Gowda Puttaswamy Nandini , Srikantaswamy Mallikarjunaswamy
Published: November 2023

The deadlock-free and live lock-free routing at the same time is minimized in the network on chip (NoC) using the proposed adoptive reconfigurable routing protocol (ARRP). Congestion condition emergencies are avoided using the proposed algorithm. The input packet distribution process is improved among all its shortest paths of output points. The performance analysis has been initiated by considering different configuration (N*N) mesh networks, by sending various ranges of data packets to the network on chip. The average and maximum power dissipation of XY, odd-even, Dy-XY algorithm, and proposed algorithm are determined. In this paper, an analysis of gate utilization during data packet transfer in various mesh configurations is carried out. The number of cycles required for each message injection in different mesh configurations is analyzed. The proposed routing algorithm is implemented and compared with conventional algorithms. The simulation has been carried out using reconfigurable two-dimensional mesh for the NoC. The proposed algorithm has been implemented considering array size, the routing operating frequency, link width length, value of probability, and traffic types. The proposed ARRP algorithm reduces the average latency, avoids routing congestion, and is more feasible for NoC compared to conventional methods.
Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform
Authors: Asmaur Rohman Budiman Putra , Suryadi Satyawan Arief , Kurniawan Dayat , Indrawijaya Ratna , Bin Ali Wael Chaeriah , Armi Nasrullah
Published: November 2023

Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar.
Hybrid chaotic map with L-shaped fractal Tromino for image encryption and decryption
Authors: Victor Juvvanapudi Sharon Rose , Rajesh Kumar Pullakura , Satyanarayana Reddy Konala Veera Venkata
Published: November 2023

Insecure communication in digital image security and image storing are considered as important challenges. Moreover, the existing approaches face problems related to improper security at the time of image encryption and decryption. In this research work, a wavelet environment is obtained by transforming the cover image utilizing integer wavelet transform (IWT) and hybrid discrete cosine transform (DCT) to completely prevent false errors. Then the proposed hybrid chaotic map with L-shaped fractal Tromino offers better security to maintain image secrecy by means of encryption and decryption. The proposed work uses fractal encryption with the combination of L-shaped Tromino theorem for enhancement of information hiding. The regions of L-shaped fractal Tromino are sensitive to variations, thus are embedded in the watermark based on a visual watermarking technique known as reversible watermarking. The experimental results showed that the proposed method obtained peak signal-to-noise ratio (PSNR) value of 56.82dB which is comparatively higher than the existing methods that are, Beddington, free, and Lawton (BFL) map with PSNR value of 8.10 dB, permutation substitution, and Boolean operation with PSNR value of 21.19 dB and deoxyribonucleic acid (DNA) level permutation-based logistic map with PSNR value of 21.27 dB.
Wide-band spectrum sensing with convolution neural network using spectral correlation function
Authors: Rajanna Anupama , Kulkarni Srimannarayana , Narasimha Prasad Sarappadi
Published: November 2023

Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
Average symbol error rate analysis of reconfigurable intelligent surfaces based free-space optical link over Weibull distribution channels
Authors: Huu Ai Duong , Tho Dang Dai , Dat Vuong Cong , Loi Nguyen Van , Ty Luong Khanh
Published: November 2023

Optical wireless communication (OWC) enables wireless connectivity using ultraviolet bands, infrared or visible. With its advantages features as high bandwidth, low cost, and operation in an unregulated spectrum. Free-space optical (FSO) communication systems are near terrestrial as a communication link between transceivers, the link is line-of-sight and successfully transmitted optical signals. Nevertheless, the optical signals transmissions over the FSO channels bring challenges to the system. To overcome the challenges posed by the FSO channels, the most common technique is to use relay stations, the most recent is the reconfigurable intelligent surfaces (RISs) technique. This study introduces a Weibull distribution model for a free-space optical communication link with RISs assisted, the parameter used to evaluate the performance of the system is the average symbol error rate (ASER). The RISs effect is examined by considering the influence of the transmitter beam waist radius, shape parameter, aperture radius, scale parameter, and signal-to-noise ratio on the ASER.
An efficient unused integrated circuits detection algorithm for parallel scan architecture
Authors: Sathyanarayana Rekha , Kanathur Ramaswamy Nataraj , Srikantaswamy Mallikarjunaswamy , Kanathur Ramaswamy Rekha
Published: November 2023

In recent days, many integrated circuits (ICs) are operated parallelly to increase switching operations in on-chip static random access memory (SRAM) array, due to more complex tasks and parallel operations being executed in many digital systems. Hence, it is important to efficiently identify the long-duration unused ICs in the on-chip SRAM memory array layout and to effectively distribute the task to unused ICs in SRAM memory array. In the present globalization, semiconductor supply chain detection of unused SRAM in large memory arrays is a very difficult task. This also results in reduced lifetime and more power dissipation. To overcome the above-mentioned drawbacks, an efficient unused integrated circuits detection algorithm (ICDA) for parallel scan architecture is proposed to differentiate the ‘0’ and ‘1’ in a larger SRAM memory array. The proposed architecture avoids the unbalancing of ‘0’ and ‘1’ concentrations in the on-chip SRAM memory array and also optimizes the area required for the memory array. As per simulation results, the proposed method is more efficient in terms of reliability, the detection rate in both used and unused ICs and reduction of power dissipation in comparison to conventional methods such as backscattering side-channel analysis (BSCA) and network attached storage (NAS) algorithm.
Reduce state of charge estimation errors with an extended Kalman filter algorithm
Authors: El Maliki Anas , Benlafkih Abdessamad , Anoune Kamal , Hadjoudja Abdelkader
Published: November 2023

Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm demonstrates remarkable accuracy and robustness in SOC estimation. The root mean square error (RMSE) analysis shows that SOC estimation errors range from only 0.30% to 2.47%, indicating substantial improvement over conventional methods. These results demonstrate the effectiveness of an EKF-based approach in overcoming external influences and providing precise SOC estimations to optimize battery management. In addition to enhancing battery performance, the results of the study may lead to the development of more reliable energy storage systems in the future. This will contribute to the wider adoption of LiBs in various applications.
Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study
Authors: Labd Zakia , Bahassine Said , Housni Khalid , Hamou Aadi Fatima Zahrae Ait , Benabbes Khalid
Published: November 2023

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.
Sentiment review of coastal assessment using neural network and naïve Bayes
Authors: Somantri Oman , Purwaningrum Santi , Maharrani Ratih Hafsarah
Published: November 2023

An assessment of a place will provide an overview for other people whether the place is feasible to be visited or not. Assessment of coastal places will provide a separate assessment for potential visitors in considering visitation. This article proposes a model using the neural network (NN) and naïve Bayes (NB) methods to classify sentiment toward coastal assessments. The proposed NN and NB models are optimized using information gain (IG) and feature weights, namely particle swarm optimization (PSO) and genetic algorithm (GA) which are carried out to increase the level of classification accuracy. Based on the experimental results, the best level of accuracy for the classification of coastal assessments is 87.11% and is named the NB IG+PSO model. The best model obtained is a model that can be used as a decision support for potential beach visitors in deciding to visit the place.
Privacy-aware secured discrete framework in wireless sensor network
Authors: Sonnappa Nandini , Muniyegowda Kempanna
Published: November 2023

Rapid expansion of wireless sensor network-internet of things (WSN-IoT) in terms of application and technologies has led to wide research considering efficiency and security aspects. Considering the efficiency approach such as data aggregation along with consensus mechanism has been one of the efficient and secure approaches, however, privacy has been one of major concern and it remains an open issue due to low classification and high misclassification rate. This research work presents the privacy and reliable aware discrete (PRD-aggregation) framework to protect and secure the privacy of the node. It works by initializing the particular variable for each node and defining the threshold; further nodes update their state through the functions, and later consensus is developed among the sensor nodes, which further updates. The novelty of PRD is discretized transmission for efficiency and security. PRD-aggregation offers reliability through efficient termination criteria and avoidance of transmission failure. PRD-aggregation framework is evaluated considering the number of deceptive nodes for securing the node in the network. Furthermore, comparative analysis proves the marginal improvisation in terms of discussed parameter against the existing protocol.
A rule-based machine learning model for financial fraud detection
Authors: Islam Saiful , Haque Md. Mokammel , Rezaul Karim Abu Naser Mohammad
Published: November 2023

Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
Survey on detecting and preventing web application broken access control attacks
Authors: Anas Ahmed , Elgamal Salwa , Youssef Basheer
Published: November 2023

Web applications are an essential component of the current wide range of digital services proposition including financial and governmental services as well as social networking and communications. Broken access control vulnerabilities pose a huge risk to that echo system because they allow the attacker to circumvent the allocated permissions and rights and perform actions that he is not authorized to perform. This paper gives a broad survey of the current research progress on approaches used to detect access control vulnerabilities exploitations and attacks in web application components. It categorizes these approaches based on their key techniques and compares the different detection methods in addition to evaluating their strengths and weaknesses. We also spotted and elaborated on some exciting research gaps found in the current literature, Finally, the paper summarizes the general detection approaches and suggests potential research directions for the future.
Deep sequential pattern mining for readability enhancement of Indonesian summarization
Authors: Maylawati Dian Sa'adillah , Kumar Yogan Jaya , Kasmin Fauziah , Ramdhani Muhammad Ali
Published: November 2023

In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect.
Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study
Authors: Slimani Hicham , El Mhamdi Jamal , Jilbab Abdelilah
Published: November 2023

Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.
Predicting automobile insurance fraud using classical and machine learning models
Authors: Shareh Nordin Shareh-Zulhelmi , Wah Yap Bee , Haur Ng Kok , Hashim Asmawi , Rambeli Norimah , Jalil Norasibah Abdul
Published: November 2023

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.
An intelligent auto-response short message service categorization model using semantic index
Authors: Padmaja Budi , Madhu Bala Myneni , Rao Patro Epili Krishna , Chaya Srikruthi Adiraju , Avinash Vytla , Sudheshna Chenumalla
Published: November 2023

Short message service (SMS) is one of the quickest and easiest ways used for communication, used by businesses, government organizations, and banks to send short messages to large groups of people. Categorization of SMS under different message types in their inboxes will provide a concise view for receivers. Former studies on the said problem are at the binary level as ham or spam which triggered the masking of specific messages that were useful to the end user but were treated as spam. Further, it is extended with multi labels such as ham, spam, and others which is not sufficient to meet all the necessities of end users. Hence, a multi-class SMS categorization is needed based on the semantics (information) embedded in it. This paper introduces an intelligent auto-response model using a semantic index for categorizing SMS messages into 5 categories: ham, spam, info, transactions, and one time password’s, using the multi-layer perceptron (MLP) algorithm. In this approach, each SMS is classified into one of the predefined categories. This experiment was conducted on the “multi-class SMS dataset” with 7,398 messages, which are differentiated into 5 classes. The accuracy obtained from the experiment was 97%.
Sintomas depressivos na infância interferem na inteligência na idade adulta?
Authors: Lima Janielle Ferreira de Brito , Lima Raina Jansen Cutrim Propp , Batalha Mônica Araújo , Silva Antônio Augusto Moura da , Ribeiro Marizélia Rodrigues Costa , Batista Rosângela Fernandes Lucena
Published: November 2023

OBJETIVO: Investigar os efeitos dos sintomas depressivos na infância no desenvolvimento intelectual do adulto jovem. MÉTODOS: Estudo realizado com uma coorte de nascimentos de São Luís, Maranhão, Brasil, composta por 339 participantes avaliados entre 7 e 9 anos e entre 18 e 19 anos. Utilizou-se modelagem de equações estruturais (escolaridade do adulto jovem, sexo, raça/cor) e variáveis da infância (estado nutricional, sintomas depressivos, função cognitiva, escolaridade do chefe da família e da mãe, renda familiar). Além disso, ocupação do chefe da família, idade da mãe e presença de companheiro foram testadas como determinantes do quociente de inteligência (QI) dos adultos. RESULTADOS: A presença de sintomas depressivos na infância gerou redução de 0,342 no desvio-padrão (DP) e -3,83 pontos no QI médio dos adultos (valor de p < 0,001). A função cognitiva na infância apresentou efeito total e direto positivo (coeficiente padronizado [CP] = 0,701; valor de p < 0,001) sobre o QI, elevando 7,84 pontos a cada aumento do nível. Identificou-se efeito indireto positivo do estado nutricional infantil (CP = 0,194; valor de p = 0,045), escolaridade do chefe da família (CP = 0,162; valor de p = 0,036) e da mãe da criança, este último mediado pela função cognitiva na infância (CP = 0,215; valor de p = 0,012) sobre o QI dos jovens. CONCLUSÃO: A presença de sintomas depressivos na infância gerou efeito negativo de longo prazo sobre a inteligência, reduzindo a pontuação do QI na idade adulta.
Novel Pan-ERR Agonists Ameliorate Heart Failure Through Enhancing Cardiac Fatty Acid Metabolism and Mitochondrial Function
Authors: Xu Weiyi , Billon Cyrielle , Li Hui , Wilderman Andrea , Qi Lei , Graves Andrea , Rideb Jernie Rae Dela Cruz , Zhao Yuanbiao , Hayes Matthew , Yu Keyang , Losby McKenna , Hampton Carissa S. , Adeyemi Christiana M. , Hong Seok Jae , Nasiotis Eleni , Fu Chen , Oh Tae Gyu , Fan Weiwei , Downes Michael , Welch Ryan D. , Evans Ronald M. , Milosavljevic Aleksandar , Walker John K. , Jensen Brian C. , Pei Liming , Burris Thomas , Zhang Lilei
Published: November 2023

BACKGROUND:

Cardiac metabolic dysfunction is a hallmark of heart failure (HF). Estrogen-related receptors ERRα and ERRγ are essential regulators of cardiac metabolism. Therefore, activation of ERR could be a potential therapeutic intervention for HF. However, in vivo studies demonstrating the potential usefulness of ERR agonist for HF treatment are lacking, because compounds with pharmacokinetics appropriate for in vivo use have not been available.

METHODS:

Using a structure-based design approach, we designed and synthesized 2 structurally distinct pan-ERR agonists, SLU-PP-332 and SLU-PP-915. We investigated the effect of ERR agonist on cardiac function in a pressure overload–induced HF model in vivo. We conducted comprehensive functional, multi-omics (RNA sequencing and metabolomics studies), and genetic dependency studies both in vivo and in vitro to dissect the molecular mechanism, ERR isoform dependency, and target specificity.

RESULTS:

Both SLU-PP-332 and SLU-PP-915 significantly improved ejection fraction, ameliorated fibrosis, and increased survival associated with pressure overload–induced HF without affecting cardiac hypertrophy. A broad spectrum of metabolic genes was transcriptionally activated by ERR agonists, particularly genes involved in fatty acid metabolism and mitochondrial function. Metabolomics analysis showed substantial normalization of metabolic profiles in fatty acid/lipid and tricarboxylic acid/oxidative phosphorylation metabolites in the mouse heart with 6-week pressure overload. ERR agonists increase mitochondria oxidative capacity and fatty acid use in vitro and in vivo. Using both in vitro and in vivo genetic dependency experiments, we show that ERRγ is the main mediator of ERR agonism-induced transcriptional regulation and cardioprotection and definitively demonstrated target specificity. ERR agonism also led to downregulation of cell cycle and development pathways, which was partially mediated by E2F1 in cardiomyocytes.

CONCLUSIONS:

ERR agonists maintain oxidative metabolism, which confers cardiac protection against pressure overload–induced HF in vivo. Our results provide direct pharmacologic evidence supporting the further development of ERR agonists as novel HF therapeutics.