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This paper presents a lightweight tensor completion algorithm with applications in wireless edge caching and hyper-spectral imaging. In wireless edge caching, the dynamic and selective nature of content popularity inevitably leads to missing observations. We address this challenge by employing tensor-based models, which are notably practical for imputing missing data. We approach cooperative caching as a problem of third-order tensor completion and prediction, acknowledging the correlations across time, files, and base stations. Given the potentially large content libraries, we modify the latent norm-based Frank-Wolfe (FW) algorithm. This modification, which incorporates multi-rank updates instead of the traditional rank-1 updates, significantly reduces computational time complexity. This advancement facilitates the development of an efficient online caching algorithm. Through simulations using Movielens datasets and comparing them with the conventional FW algorithm, our proposed algorithm demonstrates a reduced computational overhead reduction and improved normalized cache hit rates within linear prediction models. Furthermore, since hyper-spectral images often have missing or corrupted data due to various noise sources, we conduct simulations on hyper-spectral images. From the Pavia University and Pavia City datasets with noisy data, our proposed tensor completion algorithm can effectively recover the original images from heavily interfered with communication noise. The effectiveness of our proposed tensor completion algorithm across various applications, including wireless edge caching and hyper-spectral imaging, accentuates its versatility and significant utility. Its ability to adeptly handle diverse challenges underscores its value as a comprehensive solution for solving intricate data recovery problems.

Reflecting Intelligent Surfaces (RISs) are reshaping the landscape of wireless communications, particularly in the terahertz (THz) frequency bands, offering promising solutions to inherent challenges in the bands. THz communication boasts bandwidths exceeding 100 GHz, leading to data rates potentially in the terabits per second (Tbps) range, thereby making it an attractive proposition for wireless communications, imaging, and sensing. However, benefits come with challenges, including significant molecular absorption, scattering, diffraction, and hardware limitations. Moreover, as bandwidth in the THz range increases, so does the difficulty of signal processing at Nyquist rate. RIS emerges as a game-changer for 6G and beyond by providing programmable reflecting elements that can adaptively modify the phases and amplitudes of incident signals, enabling precision in directing THz waves and enhancing received signal strength. Such capabilities can significantly mitigate path loss and atmospheric absorption challenges. Furthermore, inherent pencil beamforming capabilities of RIS lead to optimized energy utilization. Major challenge in THz communications is the pressing needs for efficient algorithms for robust THz transceivers and optimizing RIS elements. This review describes the integration of RIS and near-field THz communications, highlighting their future potential and challenges for the next-generation wireless networks. In this article, a comprehensive understanding of the complexities and nuances of near-field propagation in 6G networks, especially as the technology shifts towards extremely large-scale antenna arrays (ELAA). Additionally, it will introduce the transformative potential of sub-Nyquist rate signal processing and artificial intelligence (AI) offering innovative solutions to address the inherent challenges of 6G communication, especially in channel estimation and beamforming strategies.

Intelligent reflecting surface (IRS), equipped with multiple reflective elements, is an important technology for improving the achievable rate and energy efficiency of communications. Meanwhile, rate-splitting (RS) is an approach that reduces the detrimental effect of multi-user interference in the system by splitting users’ messages into private and common parts. In this work, we use the RS method in an IRS-aided massive multiple-input multiple-output (mMIMO) system. We assume each user is equipped with multiple antennas and then design the transceiver, which include the precoder and the combiner. We analyze the performance of the achievable sum-rate when the base station (BS) and users know perfect channel state information (CSI) or only statistical CSI. We use a projected gradient descent method (PGDM) to optimize the IRS phase shifts by minimizing the mean-squared error (MSE) for data estimates when the system has mixed CSI. The performance of combiner and precoder quantization, as well as the limited feedback has also been investigated in simulations in terms of the achievable sum-rate by using the iterative chordal distance (ICD) method.

BackgroundAntiretroviral Treatment (ART) significantly reduces HIV transmission. We conducted a cost-effectiveness analysis of the impact of expanded ART in South Africa.MethodsWe model a best case scenario of 90% annual HIV testing coverage in adults 15-49 years old and four ART eligibility scenarios: CD4 count ResultsExpanding ART to CD4 count ConclusionIncreasing the provision of ART to

In 2009, Government of Kenya with key stakeholders implemented an integrated multi-disease prevention campaign for water-borne diseases, malaria and HIV in Kisii District, Nyanza Province. The three day campaign, targeting 5000 people, included testing and counseling (HTC), condoms, long-lasting insecticide-treated bednets, and water filters. People with HIV were offered on-site CD4 cell counts, condoms, co-trimoxazole, and HIV clinic referral. We analysed the CD4 distributions from a district hospital cohort, campaign participants and from the 2007 Kenya Aids Indicator Survey (KAIS). Of the 5198 individuals participating in the campaign, all received HTC, 329 (6.3%) tested positive, and 255 (5%) were newly diagnosed (median CD4 cell count 536 cells/μL). The hospital cohort and KAIS results included 1,284 initial CD4 counts (median 348/L) and 306 initial CD4 counts (median 550/μL), respectively (campaign and KAIS CD4 distributions P=0.346; hospital cohort distribution was lower P

BackgroundIntegrated disease prevention in low resource settings can increase coverage, equity and efficiency in controlling high burden infectious diseases. A public-private partnership with the Ministry of Health, CDC, Vestergaard Frandsen and CHF International implemented a one-week integrated multi-disease prevention campaign.MethodResidents of Lurambi, Western Kenya were eligible for participation. The aim was to offer services to at least 80% of those aged 15-49. 31 temporary sites in strategically dispersed locations offered: HIV counseling and testing, 60 male condoms, an insecticide-treated bednet, a household water filter for women or an individual filter for men, and for those testing positive, a 3-month supply of cotrimoxazole and referral for follow-up care and treatment.FindingsOver 7 days, 47,311 people attended the campaign with a 96% uptake of the multi-disease preventive package. Of these, 99.7% were tested for HIV (87% in the target 15-49 age group); 80% had previously never tested. 4% of those tested were positive, 61% were women (5% of women and 3% of men), 6% had median CD4 counts of 541 cell/µL (IQR; 356, 754). 386 certified counselors attended to an average 17 participants per day, consistent with recommended national figures for mass campaigns. Among women, HIV infection varied by age, and was more likely with an ended marriage (e.g. widowed vs. never married, OR.3.91; 95% CI. 2.87-5.34), and lack of occupation. In men, quantitatively stronger relationships were found (e.g. widowed vs. never married, OR.7.0; 95% CI. 3.5-13.9). Always using condoms with a non-steady partner was more common among HIV-infected women participants who knew their status compared to those who did not (OR.5.4 95% CI. 2.3-12.8).ConclusionThrough integrated campaigns it is feasible to efficiently cover large proportions of eligible adults in rural underserved communities with multiple disease preventive services simultaneously achieving various national and international health development goals.