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How Does Status of Longitudinal Arch of Feet Affect Sports Performances in Basketball Players in Iceland?
(2023) Gisladottir, Thordis; Ramos, Jorgelina; Petrović, Miloš
Basketball is a highly anaerobic game, that consists of high-intensity efforts, such as jumping, landing, change of direction and sprinting, followed by lower-intensity efforts. Postural deformities can be a limiting factor in achieving maximal performances in basketball. Flat feet are one of the most common postural deformities and they can lead to poorer performances, discomfort, and pain. In this research we investigated the differences in certain sports performances among Icelandic basketball players in relation to the status of longitudinal arch of the feet. A sample of 143 basketball players (23 girls, age 12.7±1.4 and 97 boys, age 12.9±1.4 – development group; 14 women playing in the first Icelandic division, age 21±6.6 and 9 men, age 22.2±3.7 playing in the first Icelandic division) were tested on a podoscope to establish the degree of feet flatness and all participants performed countermovement jump, drop jump, 20m sprint and T-test for agility. To assign players in group with or without flat feet, Clark's angle has been used. Except in 20m sprint test, no differences were found between the groups with and without flat feet, which indicates that feet flatness was not a limiting factor in jumping and change of direction tasks but was in sprinting. We state that only strong and healthy feet might lead to excellent performance.
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Energy and cost management of different mixing ratios and morphologies on mono and hybrid nanofluids in collector technologies
(2023) Tao, Hai; Aldlemy, Mohammed Suleman; Alawi, Omer A.; Kamar, Haslinda Mohamed; Homod, Raad Z.; Mohammed, Hussein A.; K. A. Mohammed, Mustafa; Mallah, Abdul Rahman; Al-Ansari, Nadhir; Yaseen, Zaher Mundher
The flat-plate solar collector (FPSC) three-dimensional (3D) model was used to numerically evaluate the energy and economic estimates. A laminar flow with 500 ≤ Re ≤ 1900, an inlet temperature of 293 K, and a solar flux of 1000 W/m2 were assumed the operating conditions. Two mono nanofluids, CuO-DW and Cu-DW, were tested with different shapes (Spherical, Cylindrical, Platelets, and Blades) and different volume fractions. Additionally, hybrid nanocomposites from CuO@Cu/DW with different shapes (Spherical, Cylindrical, Platelets and Blades), different mixing ratios (60% + 40%, 50% + 50% and 40% + 60%) and different volume fractions (1 volume%, 2 volume%, 3 volume% and 4 volume%) were compared with mono nanofluids. At 1 volume% and Re = 1900, CuO-Platelets demonstrated the highest pressure drop (33.312 Pa). CuO-Platelets achieved the higher thermal enhancement with (8.761%) at 1 vol.% and Re = 1900. CuO-Platelets reduced the size of the solar collector by 25.60%. Meanwhile, CuO@Cu-Spherical (40:60) needed a larger collector size with 16.69% at 4 vol.% and Re = 1900. CuO-Platelets with 967.61, CuO–Cylindrical with 976.76, Cu Platelets with 983.84, and Cu-Cylindrical with 992.92 presented the lowest total cost. Meanwhile, the total cost of CuO–Cu–Platelets with 60:40, 50:50, and 40:60 was 994.82, 996.18, and 997.70, respectively.
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Machine learning techniques and data for stock market forecasting : A literature review
(2022-07-01) Kumbure, Mahinda Mailagaha; Lohrmann, Christoph; Luukka, Pasi; Porras, Jari; Department of Computer Science
In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles.
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The contact-less method of chip-to-chip high speed data transmission monitoring
(2014-03) Zmuda, M.; Szczepanski, S.; Koziel, S.; Graczyk, S.; Department of Engineering
This paper presents a technique of decoupling differential signals transmitted in a pair of microstrip lines on a printed circuit board (PCB), using dedicated coupler for high speed data transmission monitoring in chip-to-chip interconnections. The coupler used for signal probing is overlayed on the pair of microstrip lines under test, and provides a signal to the next blocks of the measurement system without disturbing transmission in the lines. Starting from the basic configuration obtained in the previous work, we describe a procedure of re-designing the coupler for enhanced directivity so that it is suitable for high-speed data transmission monitoring. The new coupler structure has improved topology based on stepped-impedance resonators with geometry parameters adjusted using numerical optimization techniques. The correctness of the design has been confirmed by the time-domain measurements performed for real chip-to-chip communication signals, using a dedicated measurement system based on Xilinx Virtex-6 FPGA Development Kit. The presented technology can be used for reducing the cost of the product diagnostics by eliminating dedicated measurement connectors, run-time system debugging or reverse engineering.
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Updating the national average figures for likelihood and severity of harms for fire and explosions in English dwellings
(2025-11) Dawson, Henry; O’Donoghue, Peter; Khandokar, Fahmida; Grinnell, Sophie

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