Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the effect of supplier-company's long-term commitment, communication and strategy on supply chain performance in manufacturing sector

The purpose of this study is to find answers to the research problem posed, namely the implementation of the long-term cooperation strategy between suppliers and companies through supplier commitment factors with companies and supplier communication with companies that can improve the company's supply chain performance. The populations in this study are suppliers of manufacturing companies listed on the Indonesia Stock Exchange (BEI) in 2016-2020. Data were collected using a questionnaire containing questions related to research. Furthermore, in this study a theoretical model was developed by proposing 5 hypotheses to be tested using Structural Equations Modeling (SEM) using AMOS software. The results showed that the function and existence of the antecedents of the Long-Term Cooperation Strategy have a high role in determining Supply Chain Performance. Based on the influence analysis, it can be concluded that Communication has a higher influence on Supply Chain Performance compared to Commitment.
Form factor of rounded objects: the sections method

An analytical method, the sections method, is developed to build a close link between the singularities of the surface of a body and the asymptotic behaviour of its amplitude form factor at large scattering vector, q. In contrast with a sphere, for which the asymptotic behaviour is in q −2, surface singularities lead to both narrow regions, for which the amplitude form factor exhibits trailing behaviour, and extended regions, for which it exhibits a rapid decrease. A numerical study of a simple example, the fourfold truncated sphere, illustrates the usefulness of these analytical predictions.
Optimization-Based Antenna Miniaturization Using Adaptively Adjusted Penalty Factors

The continuing trend for miniaturization of electronic devices necessitates size reduction of the comprising components and circuitry. Specifically, integrated circuit-antenna modules therein require compact radiators in applications such as 5G communications, implantable and on-body devices, or internet of things (IoT). The conflict between the demands for compact size and electrical and field performance can be mitigated by means of constrained numerical optimization. Evaluation of performance-related constraints requires expensive electromagnetic (EM) analysis of the system at hand; therefore, their explicit handling is inconvenient. A workaround is the penalty function approach where the primary objective (typically, antenna size) is complemented by additional terms quantifying possible constraint violations. The penalty coefficients that determine contributions of these terms are normally adjusted manually, which hinders precise control over antenna performance figures and often leads to inferior results in terms of achieved miniaturization rates. This paper proposes a novel algorithm featuring an automated adjustment of the penalty factors throughout the optimization process. Our methodology is validated using three broadband antenna structures. The obtained results demonstrate that the presented adaptive adjustment permits a precise control over the constraint violations while leading to better miniaturization rates as compared to manual penalty term setup.
Comparison of Up-to-seven criteria with Milan Criteria for liver transplantation in patients with HCC

Objective: To evaluate the possibility of extending the liver transplantation criteria in patients with HCC beyond the Milan criteria. Materials and Methods: Fifty-four cirrhotic patients with HCC were transplanted in our institution using the up-to-seven criteria as the upper limit of transplantability. Results were compared with a similar group of 47 patients who were transplanted using the Milan criteria during the period 2006-2012. HCC recurrence and patient survival rates after liver surgery were analyzed. A comparison between the imaging before liver transplant and explanted liver was also performed. Results: Three- and five-year survival (0.83 vs. 0.78 ; 0.76 vs 0.63) and HCC recurrence (7.2% vs 8.5%) were similar between the up-to-seven and the Milan criteria groups, respectively. In the up-to-seven group, bigger lesions ( p =0.04) and more patients undergoing bridging therapies before liver transplant ( p =0.0007) were found. In both groups imaging underestimated disease extension in 9 cases (16,7% vs. 19%) and no significant difference was noted. Conclusion: Recurrence and survival in patients transplanted with up-to-seven criteria are similar. The up-to-seven criteria enable patients with more advanced HCC to undergo liver transplantation.
Chemical vapor deposited monolayer MoS2 top-gate MOSFET with atomic-layer-deposited ZrO2 as gate dielectric

For the first time, ZrO2 dielectric deposition on pristine monolayer MoS2 by atomic layer deposition (ALD) is demonstrated and ZrO2/MoS2 top-gate MOSFETs have been fabricated. ALD ZrO2 overcoat, like other high-k oxides such as HfO2 and Al2O3, was shown to enhance the MoS2 channel mobility. As a result, an on/off current ratio of over 107, a subthreshold slope of 276 mV dec−1, and a field-effect electron mobility of 12.1 cm2 V−1 s−1 have been achieved. The maximum drain current of the MOSFET with a top-gate length of 4 μm and a source/drain spacing of 9 μm is measured to be 1.4 μA μm−1 at VDS = 5 V. The gate leakage current is below 10−2 A cm−2 under a gate bias of 10 V. A high dielectric breakdown field of 4.9 MV cm−1 is obtained. Gate hysteresis and frequency-dependent capacitance–voltage measurements were also performed to characterize the ZrO2/MoS2 interface quality, which yielded an interface state density of ∼3 × 1012 cm−2 eV−1.
3D Model Identification of a Soft Robotic Neck

Soft robotics is becoming an emerging solution to many of the problems in robotics, such as weight, cost and human interaction. In order to overcome such problems, bio-inspired designs have introduced new actuators, links and architectures. However, the complexity of the required models for control has increased dramatically and geometrical model approaches, widely used to model rigid dynamics, are not enough to model these new hardware types. In this paper, different linear and non-linear models will be used to model a soft neck consisting of a central soft link actuated by three motor-driven tendons. By combining the force on the different tendons, the neck is able to perform a motion similar to that of a human neck. In order to simplify the modeling, first a system input–output redefinition is proposed, considering the neck pitch and roll angles as outputs and the tendon lengths as inputs. Later, two identification strategies are selected and adapted to our case: set membership, a data-driven, nonlinear and non-parametric identification strategy which needs no input redefinition; and Recursive least-squares (RLS), a widely recognized identification technique. The first method offers the possibility of modeling complex dynamics without specific knowledge of its mathematical representation. The selection of this method was done considering its possible extension to more complex dynamics and the fact that its impact in soft robotics is yet to be studied according to the current literature. On the other hand, RLS shows the implication of using a parametric and linear identification in a nonlinear plant, and also helps to evaluate the degree of nonlinearity of the system by comparing the different performances. In addition to these methods, a neural network identification is used for comparison purposes. The obtained results validate the modeling approaches proposed.
Bio-oil production from pyrolysis of coffee and eucalyptus sawdust in the presence of 5% hydrogen

In this paper, we have done some important analysis of bio-oil obtained from the pyrolysis of coffee and eucalyptus sawdust in the presence of 5% Hydrogen. The bio-oil was obtained in one step pyrolysis in which temperature of the system was kept 25oC and then increased up to 850oC. After pyrolysis the obtained dark sticky liquid (highly viscous biooil) was introduced to thermal cracking. During thermal cracking the bio-oil was condensed at two different temperatures 100oC and 5oC so we got two types of bio-oil, BHTT (bio-oil obtained at high temperature 100 oC after thermal cracking) and BLTT (bio-oil obtained at low temperature 5oC after thermal cracking). Then both types of bio-oil were distillated and analyzed in Gas chromatography and Mass spectrometry (GC-MS) technique and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection (GC×GC/TOFMS). Agriculture residue bio-oil and its thermal cracking fractions could be effectively characterized by GC/MS and GC×GC/TOFMS, where the light fraction was composed of a wide range of lower polarity compounds while heavy fraction had higher polarity compounds. Keywords— Chromatographic analysis, GC/MS, GC×GC/TOFMS, biomass pyrolysis, bio-oil production. Abbreviations: BESC: Bio-oil produced from Coffee and Eucalyptus Sawdust BHTT: Bio-oil condensed at100oC High Temperature after Thermal cracking BLTT: Bio-oil condensed at 5oC Low Temperature after Thermal cracking
Macroscopic‐Scale Three‐Dimensional Carbon Nanofiber Architectures for Electrochemical Energy Storage Devices

The development of high‐performance electrochemical energy storage devices is critical for addressing energy crises and environmental pollution. Hence, the design and preparation of next‐generation electrode materials have been gaining increasing attention. Recent progress has demonstrated that three‐dimensional (3D) carbon nanomaterials are extremely promising candidates for the electrodes of electrochemical energy storage devices due to their unique structural advantages of interlinked architecture. Herein, recent advances in the scalable fabrication of 3D carbon nanofiber (CNF)‐based materials and their applications for electrochemical energy storage devices are summarized. Some representational 3D CNF architectures, such as CNF gels, 3D CNF films, 3D CNF arrays, and their nanocomposites, are highlighted with regard to various applications, including supercapacitors, lithium‐ion batteries (LIBs), sodium‐ion batteries (SIBs), lithium–sulfur (Li–S), lithium–selenium (Li–Se), and metal–O2 batteries, as well as other new battery systems. Finally, contemporary challenges in the scalable fabrication of 3D CNF architectures are outlined and a brief outlook to future studies is given. This review illustrates significant opportunities for the macroscopic fabrication of 3D CNF architectures, and therefore inspires new discoveries to promote the practical applications of 3D CNF architectures in electrochemical energy storage fields.
Correlation studies between pore structure and electrochemical performance of activated polymer‐based hard carbon with various organic and aqueous electrolytes

In this study, activated polymer‐based hard carbon (APHC) was first prepared using CO2 activation. Then electrochemical measurements of APHC‐applied electrodes were used to evaluate these electrodes for application to electric double‐layer capacitors. The APHCs were prepared under different conditions of activation temperature and time. The APHCs that were activated at 1000°C for 40 and 50 minutes showed good textural properties (specific surface area: 2440 and 2210 m2/g, mesopore fraction: 43.5% and 63.1%, respectively). These properties were observed using the N2/77 K adsorption isotherm, and APHCs have favorable pore structures for ion adsorption in electrolytes. Electrochemical techniques such as galvanostatic charge/discharge, cyclic voltammetry, and electrochemical impedance spectroscopy were used to assess the capacitive behaviors and rate performance of the APHCs in various electrolytes. The specific capacitance of the APHCs showed different results depending on the electrolytes used. APHC‐10‐50 showed 117 F/g of specific capacitance in propylene carbonate (PC), but only 106 F/g in acetonitrile (AcN), very different from APHC‐10‐40 (77 F/g in PC and 120 F/g in AcN). Although APHC‐10‐50 had a smaller specific surface area than that of APHC‐10‐40, it exhibited a high mesopore fraction with pore size optimal for PC electrolyte, which has larger ions and clusters than AcN does. This can lead to a relatively higher specific capacitance of APHC‐10‐50 in PC electrolyte than that of APHC‐10‐40 due to the high mesopore fraction.
MAGNet: Machine Learning Guided Application-Aware Networking for Data Centers

Modern data centers are witnessing fast-growing east-west traffic on their network infrastructure due to the highly distributed data center applications. Motivated by the heterogeneity of such application workloads, we propose in this article an extensible network management architecture called MAGNet which enables application-aware intra-data center networking. The crux of MAGNet is the smart endpoint residing within end-hosts, which is empowered by machine learning combined with lightweight workload tracing to detect workload identities and enable workload-dependent packet tagging. The centralized management plane interface of MAGNet allows network functions to interpret packet tags and perform application-aware packet processing. We demonstrate the feasibility of the architecture via prototype implementation and extensive use case evaluation. Our experiments show that the smart endpoint can fingerprint many real-world applications with 99 percent accuracy only at 1–2 percent additional CPU, and that application-aware data plane can potentially bring substantial benefits in terms of security (e.g., via identity-based microsegmentation), CPU usage (e.g., for intrusion detection) and network latency (e.g., via TCP stack customization).
Study of the nutritional value of fermented milk drinks from goat’s milk

The primary aim of this paper is to explore the nutritional value of fermented milk drinks from a goat. A vegetable component was used as fillers, including amaranth flour, flour from kumarshik, fruit and berry filler: irga, black chokeberry, and black currant. This study’s specific objectives were to determine organoleptic and safety indicators and study the shelf life of finished fermented milk drinks from goat milk with various fillers. A detailed technological scheme for the production of fermented milk drinks was drawn up. In the study, the rational formulation of fermented milk drinks was determined using the “Search for solutions” program MS Excel.
VALIDATION OF PSYCHOLOGICAL WELL-BEING MEASUREMENT ITEMSUSING CONTENT VALIDITY RATIO TECHNIQUE

The literature has emerged that offers contradictory findings about psychological well-being but the information among education officer was limited. Until recently, there has been no reliable evidence of psychological well-being measurement using experts consensusin Malaysia. This study aims to obtainthe validity ofpsychological well-being measurement items amongSchool Improvement Specialist Coaches Plus (SISC+) and School Improvement Partner Plus (SIPartners+) officers through expert consensuswith the Content Validity Ratio or CVR methodby Lawshe (1975). This quantitative method uses a survey design through questionnaireswhilethe sampling technique includespurposive sampling,namely judgement sampling that comprisesthirteen experts inthe fields of counseling, psychology, psychometric, and language. Content validity involves fifty-four items with six dimensions adapted from Ryff’s Scales of Psychological Well-Being(1989). Overall findings recorded N=13, minimum value=0.54, and CVR=9 itemsrefined.Allpsychological well-being itemsthat have undertakencontent validitywere suggested to proposedin order to conduct a pilot studyon SISC+ and SIPartners+ officers usingthe Rasch Measurement Model.
Effects of electrochemical properties of ferrocenylpyrazolylnickel(ii) and palladium(ii) compounds on their catalytic activities in ethylene oligomerisation reactions

Palladium complexes of ferrocenylpyrazolylpyridine and ferrocenylpyrazolylamine were synthesised and screened as pre-catalysts (1–4) for olefin polymerisation. The pre-catalysts 1–4 on activation with EtAlCl2 in the presence of ethylene with chlorobenzene or hexane as solvent were highly active with 1 being the most active, with an activity of 360 kg mol Pd−1 h−1. The major product from the reaction was 1-butene and high carbon content oligomers. The molecular weight (m/z) of the high carbon content oligomers is as high as 623.0. When toluene is used as solvent, the products obtained were ethyltoluene and butyltoluene and 1-butene. The electronic properties of the ligands (L1–L7) and complexes (1–10) were determined by cyclic voltammetry (CV) and molecular modelling. The CV results show that the ferrocenyl is easily oxidized upon the introduction of pyrazolyl derivatives, the process is quasi-reversible. However, complexation of the ligands with palladium or nickel results in difficulty in oxidizing the ferrocenyl moiety. This is an indication of the electrophilic nature of both the palladium and nickel centres. The mechanism of the oxidation was observed to be diffusion-controlled and is independent of scan rate. Molecular modelling experiments show that nickel and palladium complexes have lower HOMO–LUMO gaps and high global descriptors, an indication of a highly electrophilic metal centre. A plot of the electrophilicity indices of the pre-catalysts against yield of the oligomers show a linear correlation, an indication that the electrophilicity of the metal centre plays an important role in the activity of these pre-catalysts.
Volume Fraction Effect for the Film Formation of PS/PVA Composites by Monitoring Photon Transmission Technique

Film forming of the polystrene (PS) doped Poly Vinyl Alcohol (PVA) particles were studied by photon transmission (PT) technique. The films were prepared as a mixture of PS and PVA particles at different compositions ranging from 10 to 100 wt % for the optical measurements at room temperature the film formation process was monitored by measuring the transmitted light intensity (Itr) from the samples using PT. The film formation includes two stages: void closure and interdiffusion stages. Percolation model was employed to interpret the distribution of PS particles in PVA lattice. Percolation threshold, Rc, was found to be as 0.4 and the volume fraction of PVA as a power law with exponent 1.01 which is very close to the predicted value, 1.00, by the percolation theory.
Role of Deep Brain Stimulation in Treatment Resistant Depression: A Review Article

Neuromodulation has been used in Treatment-Resistant Depression (TRD) as a presumed intervention targeting diverse brain regions however, antidepressant properties of DBS for TRD in latest clinical trials are still under debate. We review the main putative neuroanatomical targets of DBS applied for the treatment of intractable TRD in this paper. Different researches imply that stimulation parameters and neuroanatomical locations could influence DBS-related antidepressant effects. Among different neuro modulatory targets that have been studied, DBS in the neuroanatomical structures of the Subcallosal Cingulate Gyrus (SCG), Anterior Limb of Internal Capsule (ALIC), Ventral Capsule/Ventral Septum (VC/VS), Medial Forebrain Bundle (MFB), and Nucleus Accumbans (NAcc) yielded more reliable antidepressant success rates in cases with TRD. Nonetheless, more well-designed randomized double-blind, controlled trials are necessary to entirely evaluate the efficiency, safety and compatibility of these DBS targets for the management of TRD.
A Structure-Aware Storage Optimization for Out-of-Core Concurrent Graph Processing

With the huge demand for graph analytics in many real-world applications, massive iterative graph processing jobs are concurrently performed on the same graphs and suffer from significant high data access cost. To lower the data access cost toward high performance, several out-of-core concurrent graph processing solutions are recently designed to handle concurrent jobs by enabling these jobs to share the accesses of the same graph data. However, the set of active vertices in each partition are usually different for various concurrent jobs and also evolve with time, where some high-degree ones (or called hub-vertices) of these active vertices require more iterations to converge due to the power-law property of real-world graphs. In consequence, existing solutions still suffer from much unnecessary I/O traffic, because they have to entirely load each partition into the memory for concurrent jobs even if most vertices in this partition are inactive and may be shared by a few jobs. In this paper, we propose an efficient structure-aware storage system, called GraphSO, for higher throughput of the execution of concurrent graph processing jobs. It can be integrated into existing out-of-core graph processing systems to promote the execution efficiency of concurrent jobs with lower I/O overhead. The key design of GraphSO is a fine-grained storage management scheme. Specifically, it logically divides the partitions of existing graph processing systems into a series of small same-sized chunks. At runtime, these small chunks with active vertices are judiciously loaded by GraphSO to construct new logical partitions (i.e., each logical partition is a subset of active chunks) for existing graph processing systems to handle, where the most-frequently-used chunks are preferentially loaded to construct the logical partitions and the other ones are delayed to wait to be required by more jobs. In this way, it can effectively spare the cost of loading the graph data associated with the inactive vertices with low repartitioning overhead and can also enable the loaded graph data to be fully shared by concurrent jobs. Moreover, GraphSO also designs a buffering strategy to efficiently cache the most-frequently-used chunks in the main memory to further minimize the I/O traffic by avoiding repeated load of them. Experimental results show that GraphSO improves the throughput of GridGraph, GraphChi, X-Stream, DynamicShards, LUMOS, Graphene, and Wonderland by 1.4-3.5 times, 2.1-4.3 times, 1.9-4.1 times, 1.9-2.9 times, 1.5-3.1 times, 1.3-1.5 times, and 1.3-2.7 times after integrating with them, respectively.
Construction of Diverse DropBlock Branches for Person Reidentification

In this article, we propose to use the data augmentation of batch drop-block with varying dropping ratios for constructing diversity-achieving branches in person reidentification (Re-ID). Since a considerable portion of input images may be dropped, this reinforces feature learning of the un-dropped region but makes the training process hard to converge. Hence, we propose a novel double-batch-split co-training approach for remedying this problem. In particular, we show that the feature diversity can be well achieved with the use of multiple dropping branches by setting individual dropping ratio for each branch. Empirical evidence demonstrates that the proposed method performs competitively on popular person Re-ID data sets, including Market-1501, DukeMTMC-reID, and CUHK03, and the use of more dropping branches can further boost the performance. Source code is available at https://github.com/AI-NERC-NUPT/DDB.